Trends in Operative and Nonoperative Hip Fracture Management 1990–2014: A Longitudinal Analysis of Manitoba Administrative Data
Bibliographic record
Abstract
OBJECTIVES: To evaluate longitudinal trends in the use of total hip arthroplasty (THA), hemiarthroplasty (HA), internal fixation (IF), and nonoperative management and to identify individual-level factors associated with nonoperative treatment of hip fracture (HF). DESIGN: Longitudinal analysis of administrative data. SETTING: Manitoba, Canada. PARTICIPANTS: All adults who experienced nontraumatic hip fractures between 1990 and 2014 (N = 19,626; mean age 80.6, 72.3% female). MEASUREMENTS: Billing codes were used to identify surgical treatment, and trends in treatment over time were examined. Regression models were developed to identify individual factors associated with receiving nonoperative management. RESULTS: Use of THA increased from 0.6% for all HFs in 1990-94 to 5.3% in 2010-14, use of HA increased from 19.3% to 29.7%, and use of IF declined from 71.8% to 59.9% (P < .001 for all); increase in THA and HA were largest in individuals with femoral neck fracture. Nonoperative management declined from 8.3% in 1990-94 to 5.1% in 2010-14 (P < .001). Factors associated with nonoperative management included aged 90 and older, male sex, residing in a care facility before fracture, and rural residence. CONCLUSION: HF is increasingly treated with THA and HA, whereas rates of nonoperative management and IF are declining. Future efforts should focus on ensuring that all individuals are optimally triaged to the best procedure for them, with nonoperative management considered for individuals with extremely poor prefracture health.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".