Patient and system factors of mortality after hip fracture: a scoping review
Bibliographic record
Abstract
BACKGROUND: Several patient and health system factors were associated with the risk of death among patients with hip fracture. However, without knowledge of underlying mechanisms interventions to improve survival post hip fracture can only be designed on the basis of the found statistical associations. METHODS: We used the framework developed by Arksey and O'Malley and Levac et al. for synthesis of factors and mechanisms of mortality post low energy hip fracture in adults over the age of 50 years, published in English, between September 1, 2009 and October 1, 2014 and indexed in MEDLINE. Proposed mechanisms for reported associations were extracted from the discussion sections. RESULTS: We synthesized the evidence from 56 articles that reported on 35 patient and 9 system factors of mortality post hip fracture. For 21 factors we found proposed biological mechanisms for their association with mortality which included complications, comorbidity, cardiorespiratory function, immune function, bone remodeling and glycemic control. CONCLUSIONS: The majority of patient and system factors of mortality post hip fracture were reported by only one or two articles and with no proposed mechanisms for their effects on mortality. Where reported, underlying mechanisms are often based on a single article and should be confirmed with further study. Therefore, one cannot be certain whether intervening on such factors may produce expected results.
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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 | 0.008 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.014 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".