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REVIEW 4 major objections 5 minor 33 references

A Decade of Healthcare Cyber Threats: Empirical Analysis, Evidence-Based Prioritisation, and AI Threat Model

T0 review · 4 major / 5 minor · reviewed 2026-08-06 · deepseek-v4-flash

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Pith's one-line read This paper claims that healthcare attackers shifted toward stealth between 2017 and 2024—defense evasion dominated every year while persistence and initial access fell to zero—and that detection guidance is weakest exactly where that effort

desk verdict Useful corpus and a concrete detection backlog, but the headline 'stealth shift' temporal claim does not survive the paper's own methodology: it is built on ATT&CK documentation timestamps, not attack dates. read the letter →

arxiv 2608.00901 v1 pith:IZUHSQAJ submitted 2026-08-02 cs.CR

Sadia Mehrin Rahi , Ayesha Siddika , Istiyak Hasan Maruf , Adrita Rahman Tory , Muhammad Aminur Rahaman , Khondokar Fida Hasan This is my paper
classification cs.CR
keywords healthcarecybersecurityMITREATT&CKCISAKEVthreatprioritisationliving-off-the-landclinicalAIsecurityvulnerabilityexploitation
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

The paper tries to establish that the healthcare cyber threat changed in kind, not just volume: attackers concentrated on stealthy, artefact-light behavior, while detection and vulnerability guidance stayed weighted toward late-stage, artefact-heavy attacks. Using 1,214 technique-use records from 44 validated threat entities, it shows defense evasion accounted for 15–20% of observed technique use in every year from 2017 to 2024, while persistence fell from 11.2% to zero and initial access from 9.0% to zero. It then builds a three-tier prioritisation framework from the gap between threat prevalence and MITRE ATT&CK data-source coverage, identifies 42 Tier 1 techniques as immediate detection opportunities, and maps them through MITRE-published ATT&CK-to-ATLAS cross-references to clinical AI systems. If correct, the paper gives defenders a sequenced, evidence-based detection backlog and says AI-integrated clinical systems inherit the same adversary techniques without modification.

What carries the argument

The machinery is the ATT&CK technique-use corpus treated as a longitudinal artefact: 1,214 records across 333 techniques and 44 entities, each technique carrying a creation-timestamp-derived year and a count of structured data sources. The argument is carried by the joint comparison of two signals per technique—entity breadth (how many threat groups use it) and detection coverage (how many ATT&CK data sources exist for it)—which yields the three-tier priority framework, and by the CISA KEV filter applied before CVE-to-technique mapping, which restricts the vulnerability surface to 679 confirmed exploited vulnerabilities. The ATT&CK-to-ATLAS cross-references (33 MITRE-published plus inferred

What would settle it

Recompute yearly tactic shares using independent incident-report first-seen dates instead of ATT&CK entity creation timestamps; if persistence and initial access no longer decline toward zero after controlling for documentation lag and artefact-light under-reporting, the claimed stealth shift is a taxonomy artifact.

Watch

Extended reading notes

Core claim

The central discovery is a structural inversion between attacker behaviour and detection infrastructure in healthcare. Across 44 validated threat groups, malware families, and campaigns documented in MITRE ATT&CK v15.1, defense evasion was the single dominant tactic in every year from 2017 to 2024, holding 15–20% of observed technique use, while persistence, initial access, and privilege escalation each declined toward zero in the longitudinal record. The paper attributes this to attackers replacing artefact-generating techniques with living-off-the-land tradecraft such as Valid Accounts (T1078), which leaves logs indistinguishable from legitimate activity. It then shows ATT&CK detection cov

Load-bearing premise

The central shift claim assumes the year-by-year decline in persistence, initial access, and privilege escalation records reflects actual changes in attacker behavior rather than changes in when MITRE documented techniques or in how often artefact-light techniques get reported and attributed.

Editorial extensions

If this is right

  • A detection programme built on artefact-based signatures is structurally obsolete for this sector; the 12 Tier 1 techniques already covered by Sigma rules are deployable immediately at zero cost.
  • The remaining 30 Tier 1 techniques define the immediate detection engineering backlog, and Tier 2's 103 techniques define the next programme phase.
  • Vulnerability remediation and detection engineering converge at T1190: patching public-facing applications and detecting their exploitation reinforce each other, and the 188 ransomware-linked KEV CVEs also feed Tier 1 priority.
  • Defenders should not read the absence of Tier 1 Reconnaissance assignments as permission to deprioritise pre-intrusion monitoring; it is a property of the attribution model, not a measured low threat.
  • Clinical AI systems inherit the same adversary techniques as traditional healthcare IT, so Tier 1 detections address both surfaces through the same instrumentation; impact, not technique, is what differs.

Reading between the lines

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

  • A testable extension the authors left implicit: run the Tier 1 framework against live hospital SIEM telemetry and check whether the 42 priority techniques actually account for most confirmed detections; the paper names this as future work but does not do it.
  • If the documented stealth shift is real for healthcare, the same ATT&CK data-source asymmetry could be checked in other critical-infrastructure sectors; a similar inversion there would suggest the problem is generic to the ATT&CK attribution model, not specific to healthcare.
  • The single-chokepoint result at T1190 may partly reflect the coarseness of the CWE-to-ATT&CK bridge; mapping the 679 KEV-confirmed CVEs with a finer-grained taxonomy could split the converged techniques and change which remediation actions deserve Tier 1 priority.
  • The bridge to clinical AI is strongest for the 33 MITRE-published cross-references; the tactic-level alignments cover the rest of the corpus but are weaker evidence that the same techniques reach AI systems without modification.
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Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

4 major / 5 minor

Summary. The paper assembles a corpus of 1,214 ATT&CK technique-use records from 44 validated healthcare-targeting threat entities, spanning 2017–2024, together with 679 KEV-confirmed CVEs. It claims that attacker behavior shifted measurably toward stealth: defense evasion remained dominant at 15–20% of observed technique use while persistence, initial access, and privilege escalation declined to zero. It further claims a structural inversion in detection coverage, identifies T1190 as a chokepoint, proposes a three-tier prioritization framework, and extends the analysis to AI-integrated clinical systems via MITRE ATLAS.

Significance. If the longitudinal claims were supported, the paper would contribute a valuable evidence-based prioritization for healthcare defenders and a useful bridge to clinical AI threat modeling. The KEV-first CVE pipeline and the assembly of a scoped corpus from authoritative open sources are sound ideas. However, the central shift claim is not established by the data as analyzed: the temporal series is derived from ATT&CK documentation timestamps and entity associations, which the paper itself admits approximate documentation rather than operational use. The T1190 chokepoint is likewise forced by the CWE-to-ATT&CK mapping. The paper is transparent about these limitations, which is commendable, but those limitations undermine the main contribution.

major comments (4)
  1. [Sec. 3.2 and Fig. 2] The longitudinal tactic series assigns each technique a first-observed year from the ATT&CK creation timestamp of its earliest associated entity, which the paper states 'approximates documentation date rather than operational first use' (Sec. 3.2, repeated in Sec. 7). The declines in persistence, initial access, and privilege escalation to zero are therefore indistinguishable from changes in ATT&CK coverage and entity-cohort composition: newly documented entities enter with current technique lists while older entities contribute historical technique sets. Sec. 4.2 concedes that artefact-generating techniques produce fewer incident reports and hence fewer ATT&CK group associations, and those are exactly the tactics that decline. No independent attack or incident timestamp is used. The headline claim that 'attackers became harder to detect, not merely more numerous' is not supported by the
  2. [Sec. 3.3 and Sec. 7] The convergence analysis ('679 CVEs funnel through a single technique, T1190') is an artifact of the CWE-to-ATT&CK bridge. Sec. 7 admits that the bridge maps most public-facing application weaknesses to T1190. Since the pipeline uses KEV -> NVD CWE -> CWE-to-ATT&CK, the dominance of T1190 is forced by the mapping's granularity rather than an empirical property of the healthcare vulnerability surface. The chokepoint claim, which is used to justify Tier 1 priority and the main convergence conclusion, should be removed or re-derived with a finer-grained mapping.
  3. [Sec. 5.1] The detection coverage inversion is partly self-referential. Prevalence (entity breadth) and coverage (data-source count) are both derived from ATT&CK associations that the paper shows are depressed for artefact-light, pre-intrusion techniques. Low coverage and low prevalence therefore share a common cause: under-attribution. The paper acknowledges this for Reconnaissance, but does not apply the same logic to the broader inversion claim that 'detection infrastructure is weakest precisely where attackers concentrate.' The claim needs an external prevalence signal or an explicit weakening to 'documented techniques with low coverage.' As it stands, the inversion is partly an artifact of the same documentation process.
  4. [Sec. 6] The AI threat model rests on 33 MITRE-published cross-references plus tactic-level semantic equivalences inferred by the authors. The conclusion that the same adversary reaches AI systems 'through identical ATT&CK techniques' is stronger than the evidence: tactic-level alignments do not establish technique-level inheritance. The paper labels these as inferences, but the abstract and Sec. 8 present the mapping as a demonstration. Please temper the AI-extension claim to hypothesis-generating and adjust the corresponding contribution statements.
minor comments (5)
  1. [Sec. 7] The statement 'Defense evasion now exceeds one in four observed technique uses' contradicts Fig. 2, which shows defense evasion at 15–20% throughout. Clarify whether a different counting method is used.
  2. [Table 2] The table heading says 'n = 7' but the text reports the mean dwell across five active campaigns, excluding two point-in-time campaigns. Clarify the effective sample size for the mean.
  3. [References] References [20] and [21] appear unrelated to healthcare cybersecurity or ATT&CK; verify whether they are cited in the correct context or are placeholders.
  4. [Fig. 3] The text states T1190 absorbs the majority of CVE-to-technique mappings, but Fig. 3 does not show this distribution. Add a panel or table displaying the mapping distribution across techniques.
  5. [Figs. 2 and 4] The figures would benefit from exact percentage labels and a clearer legend; the current shading and small text make it hard to verify the claims at a glance.

Circularity Check

2 steps flagged · score 7.0 of 10

Longitudinal 'stealth shift' is derived from ATT&CK documentation timestamps, and the T1190 chokepoint is an artifact of the CWE-to-ATT&CK bridge; both are conceded in the paper's own limitations.

  1. self definitional [Section 3.2 (Methodology); Section 4.2 (Strategic Shift); Section 7 (Limitations)]
    "Each technique was assigned a first-observed year from the ATT&CK creation timestamp of its earliest associated entity, which approximates documentation date rather than operational first use. ... artefact-generating techniques produce fewer observable indicators, fewer incident reports, and consequently fewer ATT&CK group associations. ... ATT&CK creation timestamps approximate documentation dates rather than operational first-use dates, so adoption curves reflect intelligence publication pace as much as adversary behaviour change."

    The longitudinal series in Fig. 2 is constructed by assigning each technique to the creation timestamp of its earliest associated ATT&CK entity. Thus the year axis measures documentation/association creation, not attack activity. The headline decline—persistence 11.2% to zero, initial access 9.0% to zero, privilege escalation 7.9% to zero—is a mathematical property of the distribution of ATT&CK metadata timestamps. The paper's own caveat that artefact-generating techniques generate fewer ATT&CK associations explains why these specific tactics fall to zero: the result is produced by the entity-association and timestamp construction, not by an independent record of adversary choices. The 'stealth shift' conclusion is therefore, by construction, a restatement of ATT&CK documentation pacing an

  2. other [Section 4.3 (KEV Vulnerability Surface); Section 7 (Limitations)]
    "T1190 alone absorbs the majority of the CVE-to-technique mappings because the healthcare environment presents a large and heterogeneous public-facing attack surface. ... the CVE-to-technique mapping concentrates on T1190 because the CWE-to-ATT&CK bridge maps most public-facing application weaknesses to that technique; future work should apply finer-grained CWE taxonomies to distribute coverage across a broader technique set."

    The convergence result (679 KEV-confirmed CVEs to a single dominant technique T1190) is an output of the chosen CWE-to-ATT&CK bridge, not of independent observation of attacker behaviour. The paper states that the bridge maps most public-facing application weaknesses to T1190, so the 'majority' concentration is predetermined by mapping granularity. The finding that a common addressable chokepoint exists across the vulnerability and behavioural surfaces is therefore the mapping assumption returned as an empirical result; changing only the granularity of the CWE taxonomy would redistribute the 679 CVEs and dissolve the chokepoint. The paper concedes this directly in Section 7.

full rationale

Two load-bearing results reduce to their own data-construction choices. First, the temporal claim that attackers shifted from persistence/initial access to defense evasion is computed from ATT&CK creation timestamps of the earliest associated entity, which the paper itself calls documentation dates rather than operational first use; the accompanying admission that artefact-generating techniques produce fewer ATT&CK associations explains the decline-to-zero as an artifact of the association process. Second, the '679 CVEs funnel to T1190' convergence is produced by the coarse CWE-to-ATT&CK mapping, as the paper explicitly acknowledges. The detection-coverage inversion and ATLAS bridge are not circular in the same way: the coverage inversion is an internal comparison of ATT&CK metadata, and the ATLAS mappings are either MITRE-published or explicitly labelled inferences. The author self-citations (refs [19]–[21]) appear in a background claim about ATT&CK adoption but are not load-bearing for the main derivations; no uniqueness theorem or fitted parameter is imported. External corroboration (CrowdStrike's 62% LOTL figure, CISA Volt Typhoon) supports the claim that defense evasion is common, but it does not validate the longitudinal decline-to-zero series, which remains a construct of the ATT&CK metadata. Because the paper's central headline finding is built from timestamps that measure documentation pacing, and the chokepoint result is forced by mapping granularity, the score is 7; the explicit limitation statements are weighed as confirming, not mitigating, the circularity.

Assumptions & free parameters 3 free parameters · 5 assumptions · 1 invented entities

The central claims rest on ATT&CK's documentation process, mapping bridges, and hand-set thresholds. No independent ground truth validates the trends, and the key chokepoint (T1190) is produced by the CWE-to-TTP mapping choice rather than by independent observation.

free parameters (3)
  • Tier 1 prevalence thresholds = 10+ entities, or 6+ with <=2 data sources, plus KEV-ransomware link
    Hand-chosen cutoffs determine the 42-technique priority list. No sensitivity analysis or validation against detection outcomes is provided.
  • Thin and zero coverage thresholds = Thin <=2 data sources, zero = 0 data sources
    Arbitrary definition of detection coverage used throughout the coverage-inversion analysis in Sec 5.1.
  • Tactic coverage bands = Low <2, Moderate 2-3, Adequate 3-4.5, Strong >4.5
    Hand-set bins group tactics into qualitative coverage levels; the resulting 'inversion' depends on these boundaries.
assumptions (5)
  • domain assumption ATT&CK technique-use records are a faithful proxy for real adversary behavior
    The entire longitudinal analysis in Sec 4.2 interprets technique counts as attacker behavior, despite Sec 7 noting that timestamps approximate documentation dates.
  • domain assumption ATT&CK data-source count measures detection coverage quality
    Sec 3.4 and 5.1 equate structured data source count with detection guidance quality; data sources are not detection rules.
  • domain assumption The CWE-to-TTP bridge maps exploited vulnerabilities to attacker techniques meaningfully
    Sec 3.3 selects MITRE's bridge; Sec 7 admits it funnels most CVEs to T1190, so the chokepoint result is an artifact of this assumption.
  • domain assumption First-observed year from ATT&CK creation timestamps approximates operational first use
    Sec 3.2 assigns years this way and Sec 7 lists it as a limitation; the temporal shift cannot be separated from documentation pace.
  • domain assumption KEV-confirmed exploitation is sufficient ground truth for healthcare-relevant CVEs
    Sec 3.3 filters CVEs by healthcare vendor/product terms and ATT&CK group pages; the curated filter is not independently validated.
invented entities (1)
  • Five clinical AI attack surfaces
    purpose: Organize ATT&CK-to-ATLAS mappings into categories such as EHR and training data pipeline, clinical AI inference API, and AI model supply chain.
    These are author-defined deployment categories with no empirical data establishing them as separable attack surfaces in real hospitals.

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Cite this review

Pith. "Pith review of A Decade of Healthcare Cyber Threats: Empirical Analysis, Evidence-Based Prioritisation, and AI Threat Model." pith.science (2026). https://pith.science/paper/IZUHSQAJ

@misc{pith2026260800901,
  author       = {Pith},
  title        = {Pith review of: A Decade of Healthcare Cyber Threats: Empirical Analysis, Evidence-Based Prioritisation, and AI Threat Model},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/IZUHSQAJ}},
  note         = {Machine review of arXiv:2608.00901}
}
read the original abstract

Healthcare systems face persistent and evolving cyber threats, yet how adversarial tactics and techniques have shifted over time has not been systematically characterised using empirical, multi-source data. This paper analyses 1,214 threat records drawn from three authoritative sources: the MITRE ATT&CK behavioural framework, the CISA Known Exploited Vulnerabilities catalogue, and the NIST vulnerability database, covering 44 validated healthcare-targeting threat entities from 2017 to 2024. We show that attacker behaviour has shifted measurably toward stealth-oriented tactics: defense evasion remained the dominant tactic throughout the observation period, consistently accounting for 15-20% of observed technique use from 2017 to 2024, while persistence declined from 11.2% to zero and initial access from 9.0% to zero over the same period. We further demonstrate that existing detection guidance is structurally misaligned with where attacker effort is concentrated, with the least-covered techniques receiving the most adversarial attention. A convergence analysis links 679 confirmed exploited vulnerabilities to a single dominant behavioural technique, identifying a common addressable chokepoint across the vulnerability and behavioural surfaces. Finally, we identify 42 high-priority techniques representing immediate detection opportunities and show that this set of techniques maps directly to emerging threats against AI-integrated clinical systems.

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Reference graph

Works this paper leans on

33 extracted references · 31 canonical work pages

  1. [1]

    X-force threat intelligence index 2024

    IBM Security. X-force threat intelligence index 2024. Technical report, 2024

    work page 2024

  2. [2]

    Department of Health and Human Services

    U.S. Department of Health and Human Services. Healthcare sector cybersecurity: 2024 annual report. Technical report, Office of Information Security and HC3, 2024

    work page 2024

  3. [3]

    Report: Health care had most reported cy - berthreats in 2024, 2025

    American Hospital Association. Report: Health care had most reported cy - berthreats in 2024, 2025

    work page 2024

  4. [4]

    Data breach investigations report 2024

    Verizon. Data breach investigations report 2024. Technical report, 2024

    work page 2024

  5. [5]

    2024 healthcare data breach report

    Forescout Technologies. 2024 healthcare data breach report. Technical report, 2024

    work page 2024

  6. [6]

    Richardson and N

    D. Richardson and N. North. Ekans ransomware and ics operations, 2020

    work page 2020

  7. [7]

    Alif et. al. Quantum threat defense: A framework for migrating iot-based health- care systems. In 2025 International Conference on Sustainable Technology and Engineering (i -COSTE), pages 1 –6. IEEE, 2025

    work page 2025

  8. [8]

    Levinson et al

    J. Levinson et al. Ransomware attacks and data breaches in us health care systems. JAMA, 333(20):1800– 1801, 2025

    work page 2025

Show all 33 references
  1. [9]

    C. S. Kruse, B. Frederick, T. Jacobson, and D. K. Monticone. Cybersecurity in healthcare: A systematic review. Technology and Health Care , 25(1):1– 10, 2017

    2017

  2. [10]

    Clarke and K

    M. Clarke and K. Martin. Managing cybersecurity risk in healthcare settings. Health Services Management Research, 36(3):155– 164, 2024

    2024

  3. [11]

    Connell and S

    M. Connell and S. Vogler. Russia’s approach to cyber warfare, 2017

    2017

  4. [12]

    S. G. Finlayson et al. Adversarial attacks on medical machine learning. Science, 363(6433):1287– 1289, 2019. Accepted in the 8th International Conference on Machine Learning for Cyber Security 2026 15

    2019

  5. [13]

    Bagdasaryan et al

    E. Bagdasaryan et al. Machine learning with electronic health records is vulnerable to backdoor trigger attacks. In AAAI Workshop on Trustworthy AI for Healthcare, 2021

    2021

  6. [14]

    Sun et al

    A. Sun et al. Badclm: Backdoor attack in clinical language models for electronic health records, 2024. arXiv:2407.05213

    2024 arXiv

  7. [15]

    J. Wang, P. Zhang, and C. Yagemann. A practical framework for evaluating medical ai security, 2024. arXiv:2512.08185

    2024

  8. [16]

    Alif et. al. Quantum threat in healthcare iot: Challenges and mitigation strategies. arXiv preprint arXiv:2412.05904, 2024

    2024 arXiv

  9. [17]

    Roy et al

    S. Roy et al. Sok: The mitre att&ck framework in research and practice. IEEE Security and Privacy , 2023

    2023

  10. [18]

    B. E. Strom et al. Mitre att&ck: Design and philosophy. Technical report, MITRE Corporation, 2018

    2018

  11. [19]

    Isadm: An integrated stride, att&ck, and d3fend model for threat modeling against real-world adversaries

    Hasan et.al. Isadm: An integrated stride, att&ck, and d3fend model for threat modeling against real-world adversaries. IEEE Access, 13:217316– 217348, 2025

    2025

  12. [20]

    Exploring the potential and feasibility of time synchronization us- ing gnss receivers in vehicleto -vehicle communications

    Hasan et al. Exploring the potential and feasibility of time synchronization us- ing gnss receivers in vehicleto -vehicle communications. In Proceedings of the 49th Annual Precise Time and Time Interval Systems and Applications Meeting, pages 80–90, 2018

    2018

  13. [21]

    Hasan et. al. Retrieval of surface reflectance from noaa -avhrr satellite data. Dhaka University Journal of Engineering and Technology , 1(2):121– 124, 2011

    2011

  14. [22]

    Prc state-sponsored actors compromise and maintain persistent access to u.s

    CISA. Prc state-sponsored actors compromise and maintain persistent access to u.s. critical infrastructure. Technical report, 2024

    2024

  15. [23]

    Mitre atlas: Adversarial threat landscape for artificial- intelligence systems, 2022

    MITRE Corporation. Mitre atlas: Adversarial threat landscape for artificial- intelligence systems, 2022. https://atlas.mitre.org

    2022

  16. [24]

    Ongun et al

    T. Ongun et al. Living-off-the-land command detection using active learning. In RAID, pages 442–455, 2021

    2021

  17. [25]

    Living off the land: How attackers hide in legitimate tools

    Vectra AI. Living off the land: How attackers hide in legitimate tools. Technical report, 2024

    2024

  18. [26]

    Tory et. al. Mind the gap: Missing cyber threat coverage in nids datasets for the energy sector. In International Conference on Big Data, IoT and Machine Learning, pages 434– 447. Springer, 2025

    2025

  19. [27]

    Satvat et al

    K. Satvat et al. Extractor: Extracting attack behavior from threat reports. In IEEE EuroS&P, pages 598–615, 2021

    2021

  20. [28]

    Li et al

    Z. Li et al. Attackg: Constructing technique knowledge graph from cyber threat intelligence reports. In ESORICS, pages 589–609, 2022

    2022

  21. [29]

    Bracciale, P

    L. Bracciale, P. Loreti, and G. Bianchi. Cybersecurity vulnerability analysis of medical devices purchased by national health services. Scientific Reports , 13:19516, 2023

    2023

  22. [30]

    Amro and A

    A. Amro and A. Gawanmeh. A comparison of vulnerability feature extraction methods from textual attack patterns, 2024. arXiv:2407.06753

    2024 arXiv

  23. [31]

    Kumar, A

    R. Kumar, A. Singh, and D. Gupta. Atlas -based threat modelling for autonomous systems. In IEEE ICAS, 2023

    2023

  24. [32]

    Biggio and F

    B. Biggio and F. Roli. Wild patterns: Ten years after the rise of adversarial machine learning. Pattern Recognition , 84:317– 331, 2018

    2018

  25. [33]

    Papernot et al

    N. Papernot et al. The limitations of deep learning in adversarial settings. In IEEE EuroS&P, pages 372–387, 2016

    2016

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