Evaluating the primary‐to‐specialist referral system for elective hip and knee arthroplasty
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.029 | 0.051 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it