Prioritizing patients for elective surgery: Clinical judgement summarized by a Linear Analogue Scale
Bibliographic record
Abstract
BACKGROUND: The New Zealand health reforms have resulted in the requirement that surgeons utilize Clinical Priority Access Criteria (CPAC) to ration patient access to elective surgery. The validity of the tools used as CPAC has been challenged. An alter-native tool, the Linear Analogue Scale (LAS), is therefore used in our institution. Our objectives were to determine the variables that influence the priority score generated using the LAS, and the length of time waited by patients awaiting general surgical procedures. METHODS: A cohort of 918 patients who were listed for elective general surgical procedures at Auckland Hospital, Auckland, New Zealand between 1 July 1998 and 31 March 1999 were studied. Patients were given a priority score generated using the LAS. For each patient, the time from assessment until his or her procedure was documented. Linear and logistic regression models were used to investigate variables (age, gender, diagnosis and surgical team) that influence priority score. Cox proportional hazards models were used to investigate variables (priority score, age, gender, and diagnosis) that influence the length of time waited. RESULTS: Graphical presentation showed a pattern of priority scores falling into 'bands' for different diagnoses. Diagnosis, and to a lesser extent surgical team, influenced priority score. Survival analysis showed 'time waited' to be influenced by priority score, diagnosis, and patient age and gender. CONCLUSION: The LAS may have a useful role in the difficult sphere of patient prioritization. Its strength lies in its simplicity. Further investigation of reliability and effect on patient outcomes is required.
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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 | 0.008 | 0.056 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".