Analysis of the referral pattern and wait time for hip arthroscopy in a single payer publicly funded health care system
Bibliographic record
Abstract
RATIONALE AND OBJECTIVES: To analyse the referral pattern for hip pain and to investigate the wait time for an orthopaedic assessment by a hip arthroscopy surgeon in a single payer health care system. We hypothesized that a significant delay from time of onset of symptoms to time of assessment by a hip arthroscopy surgeon exists. METHOD: Retrospective review of prospectively collected data in an academic hospital in a single payer health care system. An electronic database analysis was conducted searching for all referrals for hip pain between February 2017 and June 2017. Data were then analysed with the aim to identify the most common reason for hip referral, calculate the duration of symptoms between onset and orthopaedic assessment, and categorize previous investigations and treatments. RESULTS: A total of 96 patients were included (47 male and 49 female). Main source of referrals was Family Medicine Physicians in 37% of cases and Primary Care Sports Medicine Physicians in 35%. The most common reason for referral was labral tear in 44.7% of cases followed by combined femoroacetabular impingement and labral tear in 21.8%. The duration of symptoms was longer than 2 years in 42% of cases and between 1 and 2 years in 40% of cases. Twenty percent of patients had previous intra-articular injection while 53% of patients had physiotherapy treatment (64% of patient underwent physiotherapy for longer than 6 months). CONCLUSION: In the Canadian single payer health care system, a significant delay from the time of onset of symptoms to the time of assessment by a hip arthroscopy surgeon exists with the vast majority of patients in our cohort waiting more than 1 year. It is unknown if this delay affects the patient outcomes. This will require further investigation. Certainly, based on our findings, we should advocate for a better screening process, centralized referrals to hip arthroscopy specialists, and appropriate patient work-up.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".