Dataset for Interpretable Machine Learning for Population-Level Tooth Loss Prediction

Reproducibility Package for the revised manuscript: Interpretable Machine Learning for Population-Level Tooth Loss Prediction Submitted to the Journal of Dental Research (JDR-26-0410, Under Review). Revision Context This package accompanies the revised manuscript submitted in response to peer review. The revision addressed all reviewer comments, including six substantive analytical additions that required new code, new outputs, and retraining of all models: Full-data retraining (Reviewer 1, Comment 3): The original 80/20 derivation split was replaced by training on 100% of BRFSS 2022 (N=433,772). Both the submitted 80/20 model and the revised full-data model are included for transparent comparison. Optimism-corrected performance (Reviewer 1, Comment 5): A 100-replicate bootstrap optimism correction was added for the full-data EBM on BRFSS 2022, reporting apparent, optimism, and optimism-corrected AUC and Brier score. Sample-size justification (Reviewer 1, Comment 7): Formal development (pmsampsize) and validation (pmvalsampsize) sample-size calculations were added following Riley et al. False-positive burden (Reviewer 1, Comment 9): A threshold operating-characteristics table was added reporting sensitivity, specificity, PPV, NPV, false-positive rate, and number needed to screen at clinically relevant probability thresholds (5%–50%) on both BRFSS 2024 and NHANES. BRFSS 2022 development performance (Reviewer 2, Comment 5): Apparent performance on the derivation dataset is now reported to enable comparison with studies that did not use external validation. Code and model sharing (Reviewers 1 and 2, Comments 8 and 4): This Zenodo package provides the complete reproducibility archive with code, trained models, and derived analytic data, fulfilling the open-data requirement. Additional manuscript-level revisions (retrospective temporal validation terminology, removal of "clinically deployable" claims, intended-use/monitoring/accountability section, abbreviation audit, supplement cross-references, non-inferiority margin justification, and descriptive Results language) are reflected in the revised manuscript text but do not require separate reproducibility artifacts. What This Package Contains This archive provides all analytic inputs, code, trained model artifacts, and final outputs needed to independently verify and reproduce the results reported in the revised manuscript. Data (8 files): Harmonized analytic CSV files derived from public-use CDC BRFSS 2022, BRFSS 2024, and NHANES 2015–2018 datasets, plus MICE-imputed Feather files from both the submitted pipeline (80/20 split) and the revised full-data pipeline. Models (6 artifacts): Both the submitted 80/20 EBM and the revised full-data EBM, locked Optuna hyperparameters, full-data MICE imputer, MICE feature list, and NHANES isotonic calibrator. Results: Optimism-corrected bootstrap estimates (100 replicates), sample-size justification (pmsampsize/pmvalsampsize with R script and pre-generated output), threshold operating characteristics at 5 probability cut-points × 2 validation datasets, old-versus-full-data EBM comparison with decision-gate summary, 7-model benchmark comparison (LR, RF, XGBoost, CatBoost, LightGBM, MLP, Stacked Ensemble), NHANES recalibration metrics (pre- and post-isotonic), and a consistency ledger mapping every number in the manuscript to its source CSV cell. Figures: Publication-ready Figure 1 (EBM feature importance), Figure 2 (ROC curves, calibration plots, decision curve analysis across 3 datasets), and Figure 3 (surveillance-to-clinical translation governance framework), with underlying plot data CSVs. Code: Two Python pipeline scripts covering MICE imputation, EBM training, benchmark evaluation, isotonic recalibration, bootstrap, threshold analysis, sample-size calculation, and figure generation. A verify_package.py script performs automated integrity checks (SHA-256 hashes, private-path scanning, and semantic numerical validation of key results). Documentation: Data dictionary with BRFSS/NHANES source-variable mappings and value coding for all 24 columns and 19 missingness indicators, data-source URLs, reproducibility notes, R session information, and a SHA-256 manifest for all 55 files. Reproduction Two scripted reproduction paths are provided (PowerShell for Windows, Bash for macOS/Linux): Quick verification from included model artifacts (~5 min): regenerates tables, figures, and the consistency ledger, then runs the automated verifier. Full rebuild from clean analytic CSVs (~2–4 hours): refits the MICE imputer on 100% of BRFSS 2022, retrains the EBM and all 7 benchmarks, reruns isotonic recalibration, recomputes 100-replicate bootstrap, and regenerates all outputs from scratch. Key Results Verified by the Package BRFSS 2022 full-data EBM apparent AUC: 0.8616 | Optimism-corrected AUC: 0.8604 BRFSS 2024 retrospective temporal validation AUC: 0.8627 | Brier: 0.0845 NHANES direct-transfer AUC: 0.7538 | Post-isotonic recalibration Brier: 0.1363 Development sample: N=433,772 (71,023 events, EPV=1,868) Software Requirements Python ≥ 3.10 (exact pinned versions in requirements_freeze.txt; minimum versions in requirements.txt) R with pmsampsize and pmvalsampsize packages (only required if rerunning the sample-size R script; pre-generated output is included) License MIT License. The included analytic files are derived from publicly available, de-identified CDC survey data (BRFSS and NHANES). Associated Publication Lam QT et al. Interpretable Machine Learning for Population-Level Tooth Loss Prediction. Journal of Dental Research. 2026. [Under Review]