Prioritizing patients for elective surgery: Clinical judgement summarized by a Linear Analogue Scale
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
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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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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