Intra-articular steroid hip injection for osteoarthritis: a survey of orthopedic surgeons in Ontario.
Bibliographic record
Abstract
BACKGROUND: Intra-articular steroid hip injection (IASHI) has been prescribed for painful hip arthritis since the 1950s, but with advances in medical and surgical management its role is less certain today. There are very few published data on the utility or prescribing patterns of IASHI. METHODS: We developed a questionnaire to seek expert opinion on IASHI that we distributed to practising Ontario-based members of the Canadian Orthopaedic Association. We systematically describe the current practices and expert opinion of 99 hip surgeons (73% response rate), focusing on indications, current use and complications experienced with IASHI. RESULTS: Only 56% of surgeons felt that IASHI was therapeutically useful, with 72% of surgeons estimating that 60% or less of their patients achieved even transient benefit from IASHI. One-quarter of the surgeons believe that IASHI accelerates arthritis progression, most of whom had stated that it would be no great loss if IASHI was no longer available. Nineteen percent of the surgeons believed that the infection rate related to total hip arthroplasty (THA) may be increased after IASHI, and this was associated with fewer IASHIs ordered per year, compared with the number prescribed by those who did not feel that infection rates would increase. CONCLUSIONS: This systematic collection of expert opinions demonstrates that substantial numbers of surgeons felt that, in their patients, IASHI was not therapeutically helpful, may accelerate arthritis progression or may cause increased infectious complications after subsequent 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 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".