Access to elective vascular surgery within the recommended time according to emergency referrals.
Bibliographic record
Abstract
BACKGROUND: Referral pattern is a potential confounding factor when waiting-list performance is reported across hospitals or periods. A common concern is the ability to accurately estimate proportions of patients undergoing surgery in the recommended time without considering emergency caseload. In this study, the relation between emergency referrals and the rate of elective admissions to hospital within the recommended time was estimated. DESIGN: A prospective cohort study. SETTING: An acute care hospital in Kingston, Ont. PATIENTS: Between 1994 and 1999, 1,173 consecutive patients accepted for elective vascular surgery. MAIN OUTCOME MEASURES: The proportion of patients who underwent surgery within the recommended time, and time to surgery. STUDY VARIABLES: The weekly number of emergency cases, enrolment periods, urgency and type of surgery. RESULTS: Overall, the proportion of patients who underwent surgery within recommended time was 0.45, (95% confidence interval [CI], 0.42-0.48). Adjusted for enrolment period, urgency and type of surgery, the estimated proportion was 0.57, (95% CI, 0.49-0.64). Compared with surgery for peripheral vascular disease, the odds of the procedure being done within the recommended time were 34% lower for aortic abdominal aneurysm repair and 41% lower for carotid endarterectomy. After adjustment for the case-mix and access attributes, the rate of elective admission within recommended time was on average 30% lower for weeks in which there were 1 to 2 emergency cases (rate ratio [RR] = 0.70, [95% CI, 0.53- 0.93]), and 39% lower for weeks with 3 or more emergency cases (RR = 0.61 [95% CI, 0.53-0.83]), relative to weeks with no emergency cases. CONCLUSIONS: When there is an increase in the number of emergency cases, a lower proportion of patients undergo elective surgery within the recommended time. Thus, when performance of surgical servces is evaluated, the probability of patients undergoing elective surgery on time should be adjusted relative to the number of emergency referrals.
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How this classification was reachedexpand
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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".