Additional file 1 of Evaluation of post-acute care and one-year outcomes among Medicare beneficiaries with hip fractures: a retrospective cohort study
Bibliographic record
Abstract
Additional file 1: Figure S1. Flow diagram of the study population. Figure S2. Inverse probability of treatment weighted one-year survival curves for fall-related injuries. Figure S3. Inverse probability of treatment weighted one-year survival curves for hospital readmission. Figure S4. Inverse probability of treatment weighted one-year survival curves for death. Figure S5. Inverse probability of treatment weighted one-year survival curves for hip fracture. Figure S6. Inverse probability of treatment weighted one-year survival curves for lower extremity fracture. Figure S7. Inverse probability of treatment weighted one-year survival curves for axial fracture. Figure S8. Inverse probability of treatment weighted one-year survival curves for upper extremity fracture. Figure S9. Inverse probability of treatment weighted one-year survival curves for intracranial bleeding. Table S1. International Classification of Diseases, Ninthand TenthRevision Codes Used to identify fall related injuries. Table S2. Standardized mean differences before and after inverse probability of treatment weighting among individuals discharged to different post-acute care settings after hip fracture hospitalization. Table S3. Risk differences and rate differences comparing one-year outcomes after discharge from post-acute care between settings following hip fracture, 2012-2018. Table S4. Risk differences and rate differences comparing one-year specific fall-related injury outcomes after discharge from post-acute care between settings following hip fracture, 2012-2018. Table S5. Associations between post-acute care setting following hip fracture and outcomes up to one year after discharge from post-acute care, 2012-2018. Table S6. Associations between post-acute care setting following hip fracture and outcomes, accounting for the competing risk of death. Table S7. E-values for quantitative bias sensitivity analyses.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.381 | 0.023 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".