Pith Number

pith:CG2JXTWA

pith:2026:CG2JXTWANWBZAT24QZ6HCSUAWM

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Isotonic Survival Regression: Calibrated Survival Distributions from Deep Cox Models

Anchit Jain, Kevin Zhang, Stephen Bates

Isotonic regression applied after a Deep Cox model calibrates survival probabilities while preserving ranking performance.

arxiv:2605.16571 v1 · 2026-05-15 · stat.ML · cs.AI · cs.LG

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4 Citations open

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The bundle contains the canonical record plus signed events. A mirror can host it anywhere and recompute the same current state with the deterministic merge algorithm.

Claims

C1strongest claim

The isotonic calibration method for Deep Cox models achieves asymptotic calibration, possesses a double-robustness property, and preserves discriminative power on synthetic and real-world clinical data.

C2weakest assumption

That applying isotonic regression post-hoc to the model's predicted survival curves will not distort the underlying risk ordering or introduce new calibration issues under the specific censoring patterns present in the target datasets.

C3one line summary

Post-hoc isotonic regression calibration for deep Cox survival models that improves calibration with theoretical guarantees including double-robustness and asymptotic calibration.

References

93 extracted · 93 resolved · 0 Pith anchors

[1] Farid E. Ahmed, Paul W. V os, and Don Holbert. Modeling survival in colon cancer: a method- ological review.Molecular Cancer, 6, 2007 2007

[2] Jishan Ahmed and Robert C. Green II. Leveraging survival analysis in cost-aware deepnet for efficient hard drive failure prediction.Neural Computing and Applications, 37, 2025 2025

[3] Publicly available clinical BERT embeddings 2019

[4] Assael, Carlo Castellani, Marisol Barao Ocampo, Patrizia Iansa, Andrea Callegaro, and Maria Grazia Valsecchi 2002

[5] Shah, and Andrew Y 2020

Formal links

2 machine-checked theorem links

Receipt and verification

First computed	2026-05-20T00:02:29.779411Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

11b49bcec06d83904f5c867c714a80b3351f33f34e496061bba71ad72dc82f8f

Aliases

arxiv: 2605.16571 · arxiv_version: 2605.16571v1 · doi: 10.48550/arxiv.2605.16571 · pith_short_12: CG2JXTWANWBZ · pith_short_16: CG2JXTWANWBZAT24 · pith_short_8: CG2JXTWA

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Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/CG2JXTWANWBZAT24QZ6HCSUAWM \
  | jq -c '.canonical_record' \
  | python3 -c "import sys,json,hashlib; b=json.dumps(json.loads(sys.stdin.read()), sort_keys=True, separators=(',',':'), ensure_ascii=False).encode(); print(hashlib.sha256(b).hexdigest())"
# expect: 11b49bcec06d83904f5c867c714a80b3351f33f34e496061bba71ad72dc82f8f

Canonical record JSON

{
  "metadata": {
    "abstract_canon_sha256": "081712ced6c9c77ce9b91731013d2ffe1bf5e6631443c9081b44507b1777f59a",
    "cross_cats_sorted": [
      "cs.AI",
      "cs.LG"
    ],
    "license": "http://creativecommons.org/licenses/by/4.0/",
    "primary_cat": "stat.ML",
    "submitted_at": "2026-05-15T19:20:23Z",
    "title_canon_sha256": "638ed385c6f94a2c0ba2e35319b9de78bc5b6af13b4f64758313aa96e7a07bef"
  },
  "schema_version": "1.0",
  "source": {
    "id": "2605.16571",
    "kind": "arxiv",
    "version": 1
  }
}