Unnecessary interventions for the management of hip osteoarthritis: a population-based cohort study
Bibliographic record
Abstract
BACKGROUND: Patients aged 40-60 years who require total hip arthroplasty (THA) often first receive unindicated hip arthroscopy or magnetic resonance imaging (MRI). Our objective was to identify potentially inappropriate resource utilization before THA, specifically reporting on the proportion of patients aged 40-60 years who underwent hip arthroscopy or MRI in the year before THA. METHODS: We conducted a retrospective, population-based study at the provincial level. We retrieved data from the Canadian Institute for Health Information (CIHI). We included all Ontario residents who underwent an elective, primary THA for osteoarthritis between Apr. 1, 2004, and Mar. 31, 2016. We identified the rates and timing of patients who underwent an MRI or hip arthroscopy before their index THA. RESULTS: The percentage of patients who underwent an MRI before THA increased significantly over the study period, from 8.7% in 2004 to 23.8% in 2015. There was also a significant but variable trend in the percentage of patients who underwent a hip arthroscopy before THA. CONCLUSION: Our results demonstrate a high, gradually increasing proportion of patients who received a hip MRI and a low but increasing proportion of patients who received hip arthroscopy in close proximity to THA. Multidisciplinary collaboration may improve knowledge translation and help reduce the rate of clinically unnecessary diagnostic and therapeutic interventions in this population of patients who require THA.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".