Evaluating the primary‐to‐specialist referral system for elective hip and knee arthroplasty
Bibliographic record
Abstract
RATIONALE, AIMS AND OBJECTIVES: Persistently long waiting times for hip and knee total joint arthroplasty (TJA) specialist consultations have been identified as a problem. This study described referral processes and practices, and their impact on the waiting time from referral to consultation for TJA. METHODS: A mixed-methods retrospective study incorporating semi-structured interviews, patient chart reviews and observational studies was conducted at three clinic sites in Alberta, Canada. A total of 218 charts were selected for analysis. Standardized definitions were applied to key event dates. Performance measures included waiting times percentage of referrals initially accepted. Voluntary (patient-related) and involuntary (health system-related) waiting times were quantified. RESULTS: All three clinics had defined, but differing, referral processing rules. The mean time from referral to consultation ranged from 51 to 139 business days. Choosing a specific surgeon for consultation rather than a next available surgeon lengthened waits by 10-47 business days. Involuntary waiting times accounted for at least 11% of total waiting time. Approximately 40-80% of the time patients with TJA wait for surgery was in the consultation period. Fifty-four per cent of new referrals were initially rejected, prolonging patient waits by 8-46 business days. CONCLUSIONS: Our results suggest that variation in referral processing led to increased waiting times for patients. The large proportion of total wait attributable to waiting for a surgical consultation makes failure to measure and evaluate this period a significant omission. Improving referral processes and decreasing variation between clinics would improve patient access to these specialist referrals in Alberta.
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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 | 0.006 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".