The study audited ten different classifiers—including linear, tree‑ensemble, neural, glass‑box, and tabular foundation models—on national health survey data to predict myocardial infarction. By systematically removing features that could cause target leakage, the authors found that all models’ AUROC scores collapsed into a narrow band, indicating that reported high accuracy in prior work was largely due to leakage rather than model sophistication. The glass‑box explainable boosting machine performed comparably to other models while being much faster, and the authors demonstrated that fairness, calibration, and uncertainty can be audited and repaired without sacrificing performance.
By Raad Bin Tareaf, Murad Al-Rajab, Samia Loucif, Samer Ellaham, Cedric Schmitz
arXiv:2606. 20115v3 Announce Type: replace Abstract: Conformal risk control (CRC) provides distribution-free segmentation guarantees by calibrating a prediction-set threshold on held-out data.
By Nafis Fuad Shahid
DeepGOF-1 is a neural goodness‑of‑fit test for logistic regression that maps residuals over covariate ranks, scores them with a frozen convolutional network, and calibrates the score via parametric bootstrap. It achieves first‑order valid level, offers explicit local power, and outperforms traditional chi‑square and projection tests in simulations and real data, revealing missed interactions and guiding model repairs. In the SUPPORT study, it identified a U‑shaped risk pattern for blood pressure and respiratory rate that standard calibration curves obscured, and it ran in 13 seconds versus hours for competing methods.
By Ebrahim Khaled Ebrahim, Osama Abd El-Aziz Hussein, Ahmed El-Kotory
arXiv:2607. 19526v1 Announce Type: new Abstract: "Stop Chasing the C-index when Evaluating Survival Analysis Models" (ICML 2026, Spotlight) argued normatively, on synthetic data, that evaluating survival models by discrimination alone, i.
By Rafael da Silva, Danilo Alvares
This systematic review and meta‑analysis examined 48 machine‑learning studies that predict live‑birth outcomes after IVF/ICSI using routine clinical data. The pooled discrimination of internally validated models was an AUC of 0.814, while externally validated models had an AUC of 0.756, with no significant difference between them. Most studies (47/48) were at high or unclear risk of bias, only four assessed calibration, and none demonstrated clinical impact, leading to a low‑to‑very‑low certainty of evidence.
By Abdul Raheem Shahzad, Muhammad Zaheer Khan, Umar Ajaib, Asad Ur Rehman, Juan Deng, Ming Zhang
The paper introduces a missingness‑aware conformal calibration method for mortality prediction that accounts for cross‑hospital distribution shifts. By selecting a measurement on an independent sample, grouping patients by whether that measurement is recorded, and applying Mondrian calibration within each group, the method avoids reusing calibration outcomes. Experiments on eICU and MIMIC‑IV data show that, compared to pooled calibration, it reduces the worst‑group coverage gap by a median of 1.9 percentage points across six settings, though the benefit varies with predictor and hospital.
By Liang You, Dongwen Ou, Hengyu Shi, Siyuan Dai
arXiv:2606. 10725v1 Announce Type: new Abstract: Background.
By Olga Shakhmatova, Dmitrii Kriukov, Daniil Larionov, Nikita Khromov, Iaroslav Bespalov, Alexander Zolotarev, Kirill Grishchenkov, Ekaterina Ivanova, Miron Kuznetsov, Ilya Sochenkov, Elizaveta Panchenko, Artem Shelmanov, Dmitry V. Dylov
PRISM is a tile‑level benchmark for pathology foundation models that evaluates reliability across four dimensions: model choice, label fraction, calibration, and out‑of‑distribution transfer. It tests eight models on six datasets at six label fractions (1%–100%) with 864 linear probe runs, plus a five‑hospital Camelyon17 transfer study. The benchmark reveals four key phenomena, including the changing relationship between AUROC and calibration at low labels, collapse of linear probes on harder datasets, the impact of source‑domain labels on target calibration, and limitations of temperature scaling.
By Alicem Koyun
arXiv:2605. 12895v2 Announce Type: replace-cross Abstract: Clinical decision-support systems are expert systems whose recommendations clinicians act on directly, yet they are usually cleared on one aggregate accuracy number from a held-out test set.
By Rohith Reddy Bellibatlu, Manpreet Singh, Yash Jajoo, Shyamal Lakhanpal, Abhishek Israni
arXiv:2609.11592v2 Announce Type: replace
Abstract: Transportation agencies increasingly predict crash-injury severity with statistical and machine-learning models, but these models do not state how...
By Amir Rafe, Subasish Das
arXiv:2606. 20115v1 Announce Type: new Abstract: Conformal risk control (CRC) provides distribution-free guarantees on segmentation quality by calibrating a prediction-set threshold on held-out data.
By Nafis Fuad Shahid
The paper introduces RouteCert, a method for ensuring risk control in multimodal systems that acquire inputs adaptively. It shows that conditional calibration can remain valid even when the acquisition policy determines the calibration group, and provides two finite‑sample constructions: threshold‑free routing with terminal‑pattern calibration and simultaneous validation of policy‑pattern pairs. Experiments on a clinical ECG task and masked multimodal benchmarks demonstrate that RouteCert achieves low disagreement rates and competitive answered fractions while validating each acquisition stage separately.
By Melika Baghi