Mortality by Timing of Hip Fracture Surgery
Bibliographic record
Abstract
In hip fracture care, it is disputed whether mortality worsens when surgery is delayed. This knowledge gap matters when hospital managers seek to justify resource allocation for prioritizing access to one procedure over another. Uncertainty over the surgical timing-death association leads to either surgical prioritization without benefit or the underuse of expedited surgery when it could save lives. The discrepancy in previous findings results in part from differences between patients who happened to undergo surgery at different times. Such differences may produce the statistical association between surgical timing and death in the absence of a causal relationship. Previous observational studies attempted to adjust for structure, process, and patient factors that contribute to death, but not for relationships between structure and process factors, or between patient and process factors. In this article, we (1) summarize what is known about the factors that influence, directly or indirectly, both the timing of surgery and the occurrence of death; (2) construct a dependency graph of relationships among these factors based explicitly on the existing literature; (3) consider factors with a potential to induce covariation of time to surgery and the occurrence of death, directly or through the network of relationships, thereby explaining a putative surgical timing-death association; and (4) show how age, sex, dependent living, fracture type, hospital type, surgery type, and calendar period can influence both time to surgery and occurrence of death through chains of dependencies. We conclude by discussing how these results can inform the allocation of surgical capacity to prevent the avoidable adverse consequences of delaying hip fracture 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 | 0.001 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".