Waiting list management: priority criteria or first‐in first‐out? A case for total joint replacement
Bibliographic record
Abstract
BACKGROUND: Total joint replacements are interventions with large waiting times from indication to the surgery management. These patients can be managed in two ways; first-in first-out or through a priority tool. The aim of this study was to compare real time on waiting list (TWL) with a priority criteria score, developed by our team, in patients awaiting joint replacement due to osteoarthritis. METHODS: Consecutive patients placed on waiting list were eligible. Patients fulfilled a questionnaire which included items of our priority tool and the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) specific questionnaire. Other priority items were extracted from the clinical history. The priority tool gives a score from 0 to 100 points, and three categories (urgent, preferent and ordinary). We studied the differences among categories and TWL by means of one-way analysis of variance. Correlational analysis was used to evaluate association among priority score and TWL and WOMAC baseline and gains at 6 months with priority score and TWL. RESULTS: We have studied 684 patients. Women represented 62% of sample. The mean age was 70 years. There were not association between the categories of priority score and TWL (P = 0.12). The rho correlation coefficient between TWL and priority score was -0.11. Among baseline WOMAC scores and priority score, the rho coefficients were 0.79, 0.7 and 0.52 with function, pain and stiffness dimensions, respectively. There were differences in the mean scores of WOMAC dimensions according to the three priority categories (P < 0.001) but no with TWL categories. Data of gains in both health-related quality of life dimensions at 6 months were similar, with differences according to priority categories but no regarding TWL. CONCLUSIONS: The results of the study support the necessity of implementing a prioritization system instead of the actual system if we want to manage the waiting list for joint replacement with clinical equity.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 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".