Automating Feedback Analysis in Surgical Training: Detection, Categorization, and Assessment

Andrew Hung; Anima Anandkumar; Arushi Gupta; Cherine Yang; Elyssa Wong; Firdavs Nasriddinov; Rafal Kocielnik

REVIEW 4 major objections 4 minor 14 references

Automating Feedback Analysis in Surgical Training: Detection, Categorization, and Assessment

T0 review · 4 major / 4 minor · reviewed 2026-08-12 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗ Review JSON

Pith's one-line read An automated speech pipeline detects trainer-to-trainee feedback from raw operating-room audio with an F1 of 0.79 on unseen surgeries, the authors report.

desk verdict End-to-end surgical feedback detection from OR audio is a real step forward, but the headline F1 rests on an under-validated GPT-4o alignment step and an ambiguous anchor protocol. read the letter →

arxiv 2412.00760 v1 pith:BUBRPGKU submitted 2024-12-01 eess.AS cs.AIcs.CLcs.ETcs.LG

Firdavs Nasriddinov , Rafal Kocielnik , Arushi Gupta , Cherine Yang , Elyssa Wong , Anima Anandkumar , Andrew Hung This is my paper · ORCID

classification eess.AScs.AIcs.CLcs.ETcs.LG

keywords surgicalfeedbackdetectionoperatingroomaudiospeakerdiarizationhallucinationremovalautomatedspeechrecognitioneducationtrainer-traineedialogueroboticsurgerytraining

verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

This paper claims that verbal feedback from a trainer to a trainee during live robotic surgery can be detected, categorized, and assessed automatically from raw operating-room audio, without human annotation of the feedback moments. On five surgeries never seen during development, the pipeline identifies feedback utterances with an F1 of 0.79 ± 0.07, and its hallucination-removal step alone accounts for roughly a 14 percent gain in detection. On the clinically relevant downstream tasks of predicting whether a trainee adjusts behavior after feedback and whether feedback is technical, the automated transcripts match or beat selective human transcription as classifier input. The authors argue this makes scalable, standardized analysis of surgical teaching possible, replacing labor-intensive manual review of full procedures.

What carries the argument

The central mechanism is a cosine-similarity gate against anchor voice embeddings. For each trainer and trainee, at least five clean anchor audio segments are selected, embedded with a pretrained speaker-embedding model, and every diarized speech segment is compared with all anchors on both sides; an average similarity below 0.2 on both sides marks the segment as a hallucination or unrelated speaker, and otherwise the segment is assigned to whichever of trainer or trainee has the higher similarity. This single step both filters the noise-inflated speech-to-text output and supplies the speaker roles that feedback detection needs, because the clinically validated definition of feedback requires a trainer addressing an actively operating trainee. Downstream, a GPT-4o classifier consumes the reconstructed dialogue together with the role labels to decide feedback presence, to predict trainee acknowledgment and behavioral adjustment, and to label feedback as anatomic, procedural, or technical.

What would settle it

Recompute feedback detection on the same five test surgeries using strict timestamp overlap between human-annotated feedback intervals and the ASR segments instead of the GPT-4o semantic alignment, and compare the F1 with the reported 0.79; if it falls to the 0.66 level of the hallucination-removal stage, the alignment step is carrying the result. Separately, audit the anchor segments for the five test surgeries: if any anchor audio overlaps the test recordings, the trainer/trainee identification numbers reflect in-domain enrollment rather than deployment behavior.

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Extended reading notes

Core claim

The paper's central claim is that a fully automated dialogue-reconstruction pipeline — voice activity detection, speaker diarization, speech-to-text, then a refinement stage that deletes hallucinated fragments and labels each utterance as trainer or trainee — can recover enough of the teaching interaction to support clinical-grade feedback analysis. The refinement stage is the key addition: it embeds each candidate speech segment and compares it by cosine similarity to few-shot anchor voice samples of the trainer and trainee, discarding segments that match neither (the hallucinations) and assigning the rest to the closer speaker. Removing hallucinations improves feedback detection F1 from 0.58 to 0.66, and adding the trainer/trainee assignment raises it to 0.79. When the reconstructed dialogue is fed to a large language model, the resulting Behavioral Adjustment predictions (F1 0.82) and Technical feedback classifications (F1 0.81) are comparable to or better than using selective human transcriptions. The paper frames the result as the first demonstration that surgical feedback can be quantified at scale from real-world recordings.

Load-bearing premise

The reported detection F1 assumes that the GPT-4o alignment step, with its five-second tolerance and semantic matching, does not inflate the match between automated transcripts and human annotations, and that the anchor voice samples identifying trainers and trainees were not taken from the same test-surgery recordings used for scoring.

Editorial extensions

If this is right

Detection of feedback from raw operating-room audio reaches F1 0.79 on unseen surgeries, so surgical teaching can be quantified without manual review of full procedures.
The hallucination-removal step contributes roughly 14 percent of detection performance, which means off-the-shelf speech recognition on operating-room audio is materially degraded by noise-generated filler utterances.
Automated transcripts with dialogue context outperform selective human transcription as input for Behavioral Adjustment prediction (F1 0.82 versus 0.78) and for Technical-feedback classification (F1 0.81 versus 0.78).
Because the pipeline relies on data organization and pretrained components rather than a surgery-specific fine-tuned model, the authors expect it to transfer to other educational clinical settings where guidance is delivered verbally.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

If the anchor samples are truly collected before surgery as enrollment, the method needs no in-procedure labeling, so a deployment version could register each trainer-trainee pair once and then process future cases unattended; the paper leaves ambiguous whether the five test surgeries used pre-surgery enrollment or anchors cut from the same recordings.
The gain attributed specifically to hallucination removal suggests that operating-room noise does not just corrupt words but fabricates short acknowledgements that mimic trainee responses, so filtering before classification may matter more than improving the recognizer itself.
With enrollment done at case start, the same refinement gate could operate in near real time and flag feedback moments within seconds of utterance, enabling in-surgery coaching analytics instead of post-hoc review.
The evaluation uses a five-second tolerance plus GPT-4o semantic matching to align automated transcripts with human annotations; a stricter timestamp-overlap benchmark would show how much of the 0.79 F1 depends on that semantic leniency.

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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Desk Editor's Note

read the letter

Worth a look. The paper builds a complete pipeline—VAD, diarization, Whisper, then a speaker-embedding filter that removes hallucinations and labels trainer/trainee turns—and measures feedback detection on 5 held-out surgeries. The central result, F1 0.79±0.07, is new: prior work required manual feedback spotting; this is the first attempt to do it end-to-end from raw audio, to my knowledge. The inclusion of confusion matrices in Appendix K is good practice; you can see the actual counts behind the ablations. The hallucination-removal step genuinely helps (F1 0.58→0.66), and the downstream evaluation on behavioral adjustment and technical feedback is a nice addition—it shows the pipeline supports clinically relevant classification at levels comparable to manual transcriptions.

Now the soft spots, in order of importance.

First, the evaluation alignment. The paper uses GPT-4o to decide whether an ASR phrase and a human-annotated feedback string 'align', with semantic matching plus a 5-second tolerance, and never validates this alignment against human judgments or checks sensitivity to the tolerance. Since human labels only record start times, there's no temporal IoU to fall back on. If the LLM is permissive, the computed F1 is optimistic. This is a real gap, and it applies to every dialogue-reconstruction row in Table 2. I don't think it reverses the conclusion—the confusion matrices show plausible precision/recall—but the paper should report a stricter alignment analysis before the 0.79 is taken at face value.

Second, the anchor protocol. Section 3.4 says anchors are chosen 'across the surgery duration'; Section 6 says clean speech is collected 'before surgery.' For the five test surgeries, that distinction matters: if anchors come from the test recordings, the Trainer/Trainee ID numbers reflect in-domain enrollment, not the deployment setting. The paper never clarifies this for the test cases.

Third, the 'surpassing human annotation' claim is overstated. The manual baseline is selective human transcription fed to the same GPT-4o prompts—not full human judgment. The authors acknowledge this in the discussion, so it's a framing issue, not a hidden flaw.

The hallucination-removal threshold is fit to a proxy (double-ASR), with low precision at the selected threshold—minor, but worth disclosing.

Bottom line: the pipeline is a solid applied contribution for surgical education and human-factors research. With a clarified anchor protocol and some sensitivity analysis on the alignment step, I'd be comfortable with it in an ML4H-style venue. Send it to reviewers; they should ask for those two fixes. I'd cite the hallucination-removal application if I worked on OR audio.

Referee Report

4 major / 4 minor

Summary. The paper proposes a fully automated pipeline for analyzing verbal feedback in surgical training. It combines voice activity detection, speaker diarization, and ASR to reconstruct surgical dialogue, then applies a novel hallucination-removal and trainer/trainee identification step based on few-shot speaker embeddings. The reconstructed dialogues are passed to GPT-4o for three clinical tasks: feedback detection, feedback effectiveness assessment, and feedback component classification. The authors evaluate on 33 real-world surgeries with a held-out test set of five unseen surgeries, reporting a feedback-detection F1 of 0.79 ± 0.07, a roughly 14% gain from hallucination removal, and downstream F1 scores of 0.82 ± 0.03 and 0.81 ± 0.03 for behavioral adjustment and technical feedback classification, respectively.

Significance. If the reported results hold, this would be a practically valuable step toward scalable, automated analysis of surgical teaching interactions. The paper has several concrete strengths: it uses a substantial real-world dataset with 4,210 clinically validated feedback annotations; it reports confusion matrices and per-step ablations in the appendices; it discloses the thresholds used in processing; it compares against multiple fixed-window baselines and an existing hallucination-removal method; and the code is publicly available. The main contribution is the integration of existing speech-processing components with a speaker-embedding-based refinement step, and the evaluation on clinically relevant downstream tasks is appropriate. However, the central detection result depends on an unvalidated LLM-based alignment procedure, and several methodological ambiguities need to be resolved before the claims can be accepted at face value.

major comments (4)

[§4.1.2, Appendix E] The reported F1 = 0.79 ± 0.07 in Table 2 depends on a GPT-4o alignment step that has not been validated against human judgments. The prompt in Appendix E asks whether two strings 'have any alignment' by common words and meaning, and Section 4.1.2 adds a 5-second tolerance; because human annotations record only feedback start times (Appendix M), there is no temporal-overlap quantity to constrain the match. A permissive semantic match can count a predicted segment as a true positive even when the predicted utterance is not the annotated feedback event, inflating both precision and recall. Please report inter-annotator agreement on the alignment judgment, a sensitivity analysis over the tolerance (e.g., 1s, 3s, 5s, 10s), and a stricter temporal-alignment baseline, and confirm that the hallucination-removal gain in Table 2 is stable under that baseline.
[§3.4, §6] There is a direct contradiction between the anchor-collection descriptions. Section 3.4 states that anchor segments are chosen 'across the surgery duration,' while Section 6 says the method 'requires collecting clean speech before surgery.' The paper never states which procedure was used for the five test surgeries. If anchors were extracted from the test recordings, then the Trainer/Trainee ID results in Table 2 include in-domain enrollment and overestimate deployment performance, where anchors would need to be collected beforehand. Please state the anchor collection procedure for the test cases explicitly, and if anchors come from the test recordings, re-evaluate with anchors from prior surgeries or assess sensitivity to the number and placement of anchors.
[Table 3, §4.2.1, §5.2, §5.3] The comparison against 'Manual annotations' is not like-for-like. The human baseline is 'Human Selective Transcription,' where annotators transcribed only the trainer's feedback, often selectively and without surrounding dialogue context, while the automated pipeline receives the full reconstructed dialogue including context (Section 4.2.1 and Discussion). The claim of surpassing human annotation in downstream tasks may therefore reflect an information asymmetry rather than superior automated analysis. Please reframe the comparison as an ablation over input context, or add a controlled baseline where the same GPT-4o classifier sees only the detected feedback phrase without dialogue context, and, if possible, a manual-annotation baseline with full transcripts.
[§3.4, Appendix J, Table 2] The cosine-similarity threshold of 0.2 used for hallucination removal is selected using a proxy label ('trivial hallucinations' defined by running ASR twice) and a 'Precision-Leaning Mean' metric, rather than on the feedback-detection task itself. The reported ~14% improvement from hallucination removal in Table 2 is therefore partly a function of a threshold tuned to a proxy, and no sensitivity analysis over this threshold is reported for the final feedback-detection F1. Please report feedback-detection F1 across a range of cosine thresholds (e.g., 0.1, 0.2, 0.3, 0.4) and confirm that the improvement is stable; also clarify the direction of the precision/recall relationship in Appendix J, where precision decreases as the threshold increases.

minor comments (4)

[Abstract, Table 2] The abstract contains a typo ('recaognition'), and Table 2's header 'T echnique' has stray spacing; please proofread the manuscript carefully.
[§4, Tables 2-3] The paper reports results on five unseen surgeries, but the fixed-window baselines are trained with five IID splits; it is unclear whether the reported ± values are standard deviations over surgeries, over splits, or over both. Please state the aggregation procedure explicitly.
[§4.2.2] Tasks 2 and 3 are evaluated only on true-positive feedback phrases from Task 1, making the downstream F1 scores conditional on detection performance. This should be stated explicitly, or end-to-end results should be reported, so readers do not interpret Table 3 as full-pipeline performance.
[Appendix J] The caption of Appendix J says the 'Precision-Leaning Mean' prioritizes precision, but the table shows precision decreasing as the threshold increases; the metric direction and the intended operating point should be clarified.

Circularity Check

0 steps flagged · score 0.0 of 10

No circular derivation: the pipeline is benchmarked against external human annotations; remaining concerns are evaluation-validity risks, not self-referential reductions.

full rationale

The paper's claims are empirical measurements of a fixed pipeline against human-annotated surgical feedback. The feedback definition and labels are imported from Wong et al. (2023), a prior study with overlapping authors, but those labels are externally produced human judgments and the test set is five unseen surgeries, so the citation is data, not a forced conclusion. The cosine threshold 0.2 is selected in Appendix J using a Koenecke et al. hallucination proxy, but the headline F1=0.79 and the ~14% improvement are computed on human feedback labels, so this is ordinary hyperparameter selection rather than a fitted parameter renamed as a prediction. The GPT-4o alignment step (App. E) and the ambiguity between anchor selection "across the surgery duration" (Sec. 3.4) and "clean speech before surgery" (Sec. 6) are legitimate threats to the validity of the reported F1, but they do not make any derived quantity equal to its inputs by construction. No equation-level or definitional reduction was found, so per the quoting standard no circular step can be exhibited.

Assumptions & free parameters 5 free parameters · 5 assumptions · 0 invented entities

The framework's central claims rest on five fitted thresholds and design choices (VAD threshold, cosine threshold, alignment tolerance, anchor count, window size), plus four domain assumptions about the reliability of human labels, embedding stability, pre-trained model transfer, and GPT-4o as a proxy for human judgment. No new physical or conceptual entities are introduced; 'hallucination' is a prior-term concept adopted from Koenecke et al.

free parameters (5)

VAD threshold = 0.3
Appendix B shows this value selected by empirical testing across 0, 0.1, 0.3, 0.5 using validation-set F1; it controls which diarized segments survive to transcription.
Cosine similarity threshold for hallucination removal = 0.2
Appendix J selects this threshold for flagging hallucinations using a proxy true-label set derived from Koenecke et al.'s double-ASR method; at this value hallucination precision is 0.063 and recall 0.933.
Evaluation alignment tolerance = 5 seconds
Section 4.1.2 applies a 5-second tolerance in the GPT-4o alignment step; no sensitivity analysis is reported for this value.
Number of anchor voice samples per speaker = at least 5
Section 3.4 fixes at least 5 anchors per person based on empirical testing (Appendix L), balancing coverage and practicality.
Fixed-window size and overlap = 10 seconds with 5-second overlap
Appendix M states this was chosen empirically based on average feedback length; affects all fixed-window baselines in Table 2.

assumptions (5)

domain assumption The Wong et al. (2023) definition of surgical feedback and the 4,210 human-annotated feedback labels are accurate ground truth.
All evaluation metrics (Tables 2 and 3) treat these annotations as the reference; any systematic miss or mislabel by the 3 raters propagates directly into the reported F1 scores.
domain assumption x-vector embeddings of speech segments remain discriminative for trainer vs trainee in noisy OR audio, and the selected anchor segments represent each speaker's stable vocal identity.
The Trainer/Trainee ID step in Section 3.4 relies on cosine similarity between segment embeddings and anchor embeddings; if OR noise or vocal strain distorts embeddings, segment assignments and hallucination filtering degrade.
domain assumption Pre-trained components (webrtcvad, pyannote speaker-diarization-3.1, Whisper, wav2vec, BERT, GPT-4o) perform as documented when applied to surgical audio.
The pipeline composes these models without fine-tuning them on OR audio (except the small classifiers in the fixed-window baselines); any domain shift in these tools affects all results.
domain assumption GPT-4o-based alignment and classification prompts yield labels that are valid proxies for the human judgments they are compared against.
Tasks 1-3 all use GPT-4o as the final classifier, and the detection evaluation uses GPT-4o to decide whether ASR output aligns with human annotations; the paper does not measure GPT-4o's own error rate on these prompts.
standard math McNemar's test assumptions (paired binary predictions) hold for the ablation comparisons.
Used for the significance claims in Tables 2 and 3.

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Cite this review

Pith. "Pith review of Automating Feedback Analysis in Surgical Training: Detection, Categorization, and Assessment." pith.science (2026). https://pith.science/paper/BUBRPGKU

@misc{pith2026241200760,
  author       = {Pith},
  title        = {Pith review of: Automating Feedback Analysis in Surgical Training: Detection, Categorization, and Assessment},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/BUBRPGKU}},
  note         = {Machine review of arXiv:2412.00760}
}

read the original abstract

This work introduces the first framework for reconstructing surgical dialogue from unstructured real-world recordings, which is crucial for characterizing teaching tasks. In surgical training, the formative verbal feedback that trainers provide to trainees during live surgeries is crucial for ensuring safety, correcting behavior immediately, and facilitating long-term skill acquisition. However, analyzing and quantifying this feedback is challenging due to its unstructured and specialized nature. Automated systems are essential to manage these complexities at scale, allowing for the creation of structured datasets that enhance feedback analysis and improve surgical education. Our framework integrates voice activity detection, speaker diarization, and automated speech recaognition, with a novel enhancement that 1) removes hallucinations (non-existent utterances generated during speech recognition fueled by noise in the operating room) and 2) separates speech from trainers and trainees using few-shot voice samples. These aspects are vital for reconstructing accurate surgical dialogues and understanding the roles of operating room participants. Using data from 33 real-world surgeries, we demonstrated the system's capability to reconstruct surgical teaching dialogues and detect feedback instances effectively (F1 score of 0.79+/-0.07). Moreover, our hallucination removal step improves feedback detection performance by ~14%. Evaluation on downstream clinically relevant tasks of predicting Behavioral Adjustment of trainees and classifying Technical feedback, showed performances comparable to manual annotations with F1 scores of 0.82+/0.03 and 0.81+/0.03 respectively. These results highlight the effectiveness of our framework in supporting clinically relevant tasks and improving over manual methods.

Figures

Figures reproduced from arXiv: 2412.00760 by the authors.

**Figure 1.** Overview of our automated surgical feedback detection and assessment framework, organized into: Dialogue Reconstruction, which integrates (A) Voice Activity Detection (VAD) to detect timespans of speech. (B) Speaker Diarization (DIA) to differentiate speech from different speakers. (C) Automated Speech Recognition (ASR) to transcribe the audio into text. Dialogue Refinement focuses on (D) Hallucination Removal & Tra… view at source ↗

**Figure 2.** Cosine similarity between trainer and trainee anchor embeddings for surgery 1. Trainer1, ..., Trainer5 refer to the different audio examples for the same trainer and likewise for Trainee1, ..., Trainee 5. Appendix M. Detailed Temporal Event Detection This approach relies on a moving fixed-length window of 10 seconds length with 5 sec overlap between the windows. These settings have been chosen empirically based on… view at source ↗

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Reference graph

Works this paper leans on

14 extracted references · 12 canonical work pages

[1]

Anatomic: familiarity with anatomic structures and landmarks. i.e. ’Stay in the correct plane, be- tween the 2 fascial layers.’

work page
[2]

Procedure: pertains to timing and sequence of surgical steps. i.e. ’You can switch to the left side now.’

work page
[3]

Technical: performance of a discrete task with appropriate knowledge of factors including exposure, instruments, and traction. i.e. ’Buzz it.’

work page
[4]

Praise: a positive remark. i.e. ’Good job.’

work page
[5]

Criticism: a negative remark. i.e. ’It should never be like this.’ User Prompt: Classify whether the following phrase contains the delivery of feedback considering the given context of the last couple turns in the dia- logue where the phrase is the last entry in the context. Format your response as follows. DO NOT DO ANY OTHER FORMATTING.: {’feedback’: ’y...

work page
[9]

For example: - If the trainer suggests tightening a suture, the trainee immediately pulls the suture thread more tightly

Behavioral Change: This refers to any physical or observable adjustment made by the trainee that directly corresponds to the feedback received. For example: - If the trainer suggests tightening a suture, the trainee immediately pulls the suture thread more tightly. Your role is to predict which type(s) of response the trainee is likely to give based on th...

work page
[10]

Examples include: - ”Okay, I see” - ”Uh-huh, got it” - ”Understood” - ”Yes, I’ll do that”

Verbal Acknowledgement: This includes any verbal or audible confirmation from the trainee indi- cating they have heard and understood the feedback. Examples include: - ”Okay, I see” - ”Uh-huh, got it” - ”Understood” - ”Yes, I’ll do that”

work page
[11]

For example: - If the trainer suggests tightening a suture, the trainee immediately pulls the suture thread more tightly

Behavioral Change: This refers to any physical or observable adjustment made by the trainee that directly corresponds to the feedback received. For example: - If the trainer suggests tightening a suture, the trainee immediately pulls the suture thread more tightly. Your role is to predict which type(s) of response the trainee is likely to give based on th...

work page

Show all 14 references

[14]

Technical: Performance of a discrete task with appropriate knowledge of factors including exposure, instruments, and traction. Examples include: - ”Ad- just the tension on the suture.” - ”Buzz it.” Your role is to predict which type(s) of feedback the phrase contains based on ...
[15]

Examples include: - ”Stay in the correct plane, between the 2 fascial layers.” - ”Avoid the blood vessels here.”

Anatomic: Familiarity with anatomic structures and landmarks. Examples include: - ”Stay in the correct plane, between the 2 fascial layers.” - ”Avoid the blood vessels here.”
[16]

Examples include: - ”You need to suture this area first.” - ”You can switch to the left side now.”

Procedural: Pertains to the timing and sequence of surgical steps. Examples include: - ”You need to suture this area first.” - ”You can switch to the left side now.”
[17]

Technical: Performance of a discrete task with appropriate knowledge of factors including exposure, instruments, and traction. Examples include: - ”Ad- just the tension on the suture.” - ”Buzz it.” Your role is to predict which type(s) of feedback the phrase contains based on ...

2024
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Springer, 2023. Juan M. Coria, Herv´ e Bredin, Sahar Ghannay, and Sophie Rosset. A Comparison of Metric Learning Loss Functions for End-To-End Speaker Verifica- tion. In Luis Espinosa-Anke, Carlos Mart ´ ın-Vide, and Irena Spasi´ c, editors,Statistical Language and Speech Proc...

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Allison Koenecke, Anna Seo Gyeong Choi, Katelyn X Mei, Hilke Schellmann, and Mona Sloane

PMLR, 2023. Allison Koenecke, Anna Seo Gyeong Choi, Katelyn X Mei, Hilke Schellmann, and Mona Sloane. Careless whisper: Speech-to-text hallucination harms. In The 2024 ACM Conference on Fairness, Account- ability, and Transparency, pages 1672–1681, 2024. Korbinian Kuhn, Verena...

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Pith tools

Reviewed August 12, 2026 · model on record in the stance chip above.

[1] [1]

Anatomic: familiarity with anatomic structures and landmarks. i.e. ’Stay in the correct plane, be- tween the 2 fascial layers.’

work page

[2] [2]

Procedure: pertains to timing and sequence of surgical steps. i.e. ’You can switch to the left side now.’

work page

[3] [3]

Technical: performance of a discrete task with appropriate knowledge of factors including exposure, instruments, and traction. i.e. ’Buzz it.’

work page

[4] [4]

Praise: a positive remark. i.e. ’Good job.’

work page

[5] [5]

Criticism: a negative remark. i.e. ’It should never be like this.’ User Prompt: Classify whether the following phrase contains the delivery of feedback considering the given context of the last couple turns in the dia- logue where the phrase is the last entry in the context. Format your response as follows. DO NOT DO ANY OTHER FORMATTING.: {’feedback’: ’y...

work page

[6] [9]

For example: - If the trainer suggests tightening a suture, the trainee immediately pulls the suture thread more tightly

Behavioral Change: This refers to any physical or observable adjustment made by the trainee that directly corresponds to the feedback received. For example: - If the trainer suggests tightening a suture, the trainee immediately pulls the suture thread more tightly. Your role is to predict which type(s) of response the trainee is likely to give based on th...

work page

[7] [10]

Examples include: - ”Okay, I see” - ”Uh-huh, got it” - ”Understood” - ”Yes, I’ll do that”

Verbal Acknowledgement: This includes any verbal or audible confirmation from the trainee indi- cating they have heard and understood the feedback. Examples include: - ”Okay, I see” - ”Uh-huh, got it” - ”Understood” - ”Yes, I’ll do that”

work page

[8] [11]

For example: - If the trainer suggests tightening a suture, the trainee immediately pulls the suture thread more tightly

Behavioral Change: This refers to any physical or observable adjustment made by the trainee that directly corresponds to the feedback received. For example: - If the trainer suggests tightening a suture, the trainee immediately pulls the suture thread more tightly. Your role is to predict which type(s) of response the trainee is likely to give based on th...

work page