Who's next? A new process for creating points systems for prioritising patients for elective health services
Bibliographic record
Abstract
We describe a new process for creating points systems for prioritising patients for elective health services. Beginning in 2004, the authors were closely involved in a project to develop the process, initially for coronary artery bypass graft surgery and then successively for other elective services. The project was led by New Zealand's Ministry of Health in collaboration with the relevant clinical professional organisations. The objective was to overcome the limitations of earlier methodologies and to create points systems that are valid and reproducible and based on a consensus of clinical judgements. As the project progressed and the process was refined, other points systems were successively created (and clinically endorsed) for hip and knee replacements, varicose veins surgery, cataract surgery, gynaecology, plastic surgery, otorhinolaryngology, and heart valve surgery. Other points systems are planned for the future. Since 2008 the process has also been used in the public health systems of Canada's western provinces. The process is explained in a step-by-step manner so that others are able to follow it to create their own points systems if desired
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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 | 0.146 | 0.147 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.006 | 0.009 |
| Scholarly communication | 0.014 | 0.018 |
| Open science | 0.004 | 0.013 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.008 | 0.004 |
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".