Constructing an episode of care from acute hospitalization records for studying effects of timing of hip fracture surgery
Bibliographic record
Abstract
Episodes of care defined by the event of hip fracture surgery are widely used for the assessment of surgical wait times and outcomes. However, this approach does not consider nonoperative deaths, implying that survival time begins at the time of procedure. This approach makes treatment effect implicitly conditional on surviving to treatment. The purpose of this article is to describe a novel conceptual framework for constructing an episode of hip fracture care to fully evaluate the incidence of adverse events related to time after admission for hip fracture. This admission-based approach enables the assessment of the full harm of delay by including deaths while waiting for surgery, not just deaths after surgery. Some patients wait until their conditions are optimized for surgery, whereas others have to wait until surgical service becomes available. We provide definitions, linkage rules, and algorithms to capture all hip fracture patients and events other than surgery. Finally, we discuss data elements for stratifying patients according to administrative factors for delay to allow researchers and policymakers to determine who will benefit most from expedited access to surgery.
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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 | 0.022 | 0.124 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".