Variations in treatment of femoral neck fractures in Alberta.
Bibliographic record
Abstract
OBJECTIVES: To examine, in the province of Alberta, temporal trends, regional variations in treatment options and in-hospital death rates after a femoral neck fracture. DESIGN: A retrospective cohort study. PATIENTS: Six years' data were abstracted from the Alberta Morbidity File, the Alberta Health Stakeholder File and the Alberta Health Care Claims File. Patients were included if they were Alberta residents, aged 65 years or older, had sustained a femoral neck fracture and had undergone internal fixation, hemiarthroplasty or total hip arthroplasty. MAIN OUTCOME MEASURES: Death rates, arthroplasty rates and hospital stay. RESULTS: In-hospital death rates were similar across hospitals, with risks being higher for men, patients aged 80 years or older and those with more comorbid conditions. Arthroplasty rates varied from 58% to 77% among hospitals, and hospital stays associated with arthroplasty were significantly longer than those associated with internal fixation. The chance of undergoing arthroplasty varied from hospital to hospital by gender and by the number of comorbid conditions. CONCLUSION: Regional variations suggest lack of agreement among Alberta's surgeons as to how best to treat femoral neck fractures.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".