Bibliographic record
Abstract
INTRODUCTION Hip fractures rank in the top 10 of all-cause disability worldwide, and the number of hip fractures is expected to rise to more than 6 million per year by 2050.1,2 Demographic projections by Statistics Canada indicate that, by the year 2036, 1 in 4 Canadians will be older than 653 years and that the annual number of hip fractures is likely to exceed 88,000 in Canada and 500,000 in the United States.1,4–6 Over half of hip fractures (54%) are femoral neck fractures, two-thirds of which are typically displaced and treated with arthroplasty, whereas one-third are undisplaced and treated with internal fixation. We conducted 2 multicenter, randomized controlled trials focusing on the management options for fractures of the femoral neck. The FAITH (Fixation using Alternative Implants for the Treatment of Hip fractures) trial compared the intervention of cancellous screws with a sliding hip screw in 1079 patients 50 years of age or older with a low-energy displaced or undisplaced femoral neck fracture, while the HEALTH (Hip Fracture Evaluation with Alternatives of Total Hip Arthroplasty vs. Hemiarthroplasty) trial compared the intervention of total hip arthroplasty with hemiarthroplasty in 1441 patients 50 years of age or older with low-energy displaced femoral neck fractures.7,8 Please refer to the Study Summaries portion of the supplement for further details on these 2 trials. The data collected as part of the FAITH and HEALTH trials provide a unique opportunity to answer highly relevant clinical questions in this fracture population. The following supplement is dedicated to addressing 12 highly relevant clinical questions using data from the FAITH and HEALTH trials.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.532 | 0.346 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".