Coronary artery bypass graft surgery in New Zealand's Auckland region: a comparison between the clinical priority assessment criteria score and the actual clinical priority assigned.
Bibliographic record
Abstract
AIMS: To describe the cohort of patients waiting for Coronary Artery Bypass Graft (CABG) surgery in the Auckland region; compare the Clinical Priority Assessment Criteria (CPAC) score with the actual priority assigned; and to assess the impact of a patient's demographic characteristics on the CPAC score and the assigned priority. METHODS: An electronic register was developed to capture all patients who had a CPAC form completed for isolated CABG surgery during the period June 2002 to September 2004 in the Auckland region. CPAC scores and clinical priority assigned were collected from the CABG booking form. Demographic characteristics came from the booking form (age, gender) or linkage via the National Health Index (NHI) number (ethnicity, deprivation score). RESULTS: The cohort displayed severe coronary artery disease and symptoms: 70% had class 3 or class 4 angina; 89% had their ability to work, live independently, or care for dependents threatened; 65% had three-vessel coronary disease; and 26% had left-main coronary disease. The CPAC score correlated only modestly with the actual clinical priority assigned, with an extremely wide range of scores for any given clinical priority. The mean CPAC score varied by the age of the patient, level of deprivation, and ethnicity--with higher mean scores among male patients who were Maori, Pacific, or more socioeconomically deprived. Clinical priority varied less by demographic characteristics than did the CPAC score, except more women than men were assigned the 'emergency' category. Despite higher CPAC scores for Maori and Pacific men, these did not translate to greater urgency in clinical priority. CONCLUSIONS: The CPAC scoring system is used to limit access onto the CABG surgery waiting list in Auckland, but is not used to prioritise patients as to the urgency of surgery once on the list. The challenge is to determine why clinicians do not consider that the CPAC score is adequate to prioritise the urgency of surgery and to build in a process whereby any such score can be continuously evaluated and improved. We have demonstrated that the establishment of an electronic register of such patients can provide timely analysis of patterns of practice and could be used on a national scale to improve future CPAC scoring systems.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".