In-hospital hip fractures: prevalence, risk factors and outcome
Bibliographic record
Abstract
SIR—The incidence of hip fractures is increasing worldwide [1] and despite the advances in perioperative care during the last 20 years a decrease in mortality after surgical treatment has not been observed [2, 3]. Demographic studies from 1986 documented that the risk of a hip fracture was 11 times greater in hospitalised patients than for non-hospitalised persons of comparable age, and that 24% of all hip fractures occurred in elderly patients hospitalised for other reasons [4]. That in-hospital fractures still present a significant problem today is highlighted by Canadian data (2000–2003), where 0.9/1000 admissions of elderly patients suffered a hip fracture during hospitalisation [5]. These data, however, offer no description of preoperative characteristics, fall circumstances, preventive measures or outcome for these patients as opposed to the general hip fracture population. Therefore, the aims of the present study were four-fold: to establish the prevalence of in-hospital fractures among the total hip fracture population in a large urban general hospital with both acute and rehabilitation services, to determine the circumstances of the fall leading to the fracture, to describe the use of pre-fracture preventive measures undertaken and to examine how these patients differed in preoperative characteristics and postoperative outcome from the remaining hip fracture population.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".