Femoral Neck Fractures: Evidence Versus Beliefs About Predictors of Outcome
Bibliographic record
Abstract
It is unclear whether current practice reflects current evidence on predictors of clinically important outcomes like mortality and fixation failure. Knowledge of predictors of outcome can and should influence treatment decisions and can subsequently improve outcomes. We hypothesized that there is evidence about the significance of predictors of outcome not being considered in the decision making process in the treatment of hip fractures because many surgeons are unaware of it. We surveyed 298 North American and European orthopedic surgeons to examine their training and experience and their opinion on the relative importance of predictors of outcome of femoral neck fracture treatment. We compared the results with the highest level of therapeutic and prognostic evidence currently available. Surgeons' perceptions about the importance of the quality of fracture reduction, patient comorbidities, degree of fracture displacement, dementia, and prefracture walking ability were justified by the current literature. However, we further identified a number of variables deemed unimportant to surgeons that have evidence to support their use in managing patients with hip fractures, including the type of anesthesia as a modifiable variable. In contrast to surgeons' perceptions, the available evidence suggests regional anesthesia is associated with a lower mortality risk than general anesthesia.
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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 | 0.029 | 0.157 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.004 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| 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".