Patient and system factors of time to surgery after hip fracture: a scoping review
Bibliographic record
Abstract
OBJECTIVES: It is disputed whether the time a patient waits for surgery after hip fracture increases the risk of in-hospital death. This uncertainty matters as access to surgery following hip fracture may be underprioritised due to a lack of definitive evidence. Uncertainty in the available evidence may be due to differences in characteristics of patients, their injury and their care. We summarised the literature on patients and system factors associated with time to surgery, and collated proposed mechanisms for the associations. METHODS: for synthesis of factors and mechanisms of time to surgery after hip fracture in adults aged >50 years, published in English, between 1 January 2000 and 28 February 2017, and indexed in MEDLINE, EMBASE, CINAHL or Ageline. Proposed mechanisms for reported associations were extracted from discussion sections. RESULTS: We summarised evidence from 26 articles that reported on 24 patient and system factors of time to surgery post hip fracture. In total, 16 factors were reported by only one article. For 16 factors we found proposed mechanisms for their association with time to surgery which included surgical readiness, available resources, prioritisation and out-of-hours admission. CONCLUSIONS: We identified patient and system factors associated with time to surgery after hip fracture. This new knowledge will inform evaluation of the putative timing-death association. Future interventions should be designed to influence factors with modifiable mechanisms for delay.
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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 | 0.006 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.009 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".