Unplanned emergency surgery in relation to length of wait lists at registration.
Bibliographic record
Abstract
OBJECTIVE: To compare the cumulative incidence of emergency surgery between two groups of patients classified according to the length of wait lists at the time of their registration for coronary artery bypass grafting (CABG) and to test for significant differences in the risk of emergency surgery resulting from registration on a longer wait list. METHODS: A prospective study of all adult British Columbia residents who registered to undergo isolated CABG. We compared the time-dependent cumulative incidence for undergoing planned surgery through unplanned emergency admission before or during a certain wait-list week between two categories of wait-list size. The list size was a simple count of patients with higher or equal urgency to undergo CABG who were on a wait list at the time of registration of a new patient. RESULTS: Wait lists with one month or less of clearance time were observed in all urgent patients and were more prevalent in semi-urgent than non-urgent patients (79.1% vs 44.7%, respectively). The patients registered on a list with a clearance time of more than one month had a rate of unplanned emergency admission similar to those on a list with a clearance time of one month or less, OR = 1.07 (95% CI, 0.78-1.47) after adjustment for age, sex, comorbidity, calendar period, urgency and week on the list. During fifty-two weeks of the wait-list follow-up, an equal proportion of patients underwent unplanned emergency surgery after registration on lists in both clearance-time categories, OR = 1.03 (95% CI, 0.78-1.37) after adjustment. The number of patients who underwent CABG without having been registered on a wait list in the same hospital exerted no independent effect. CONCLUSIONS: The length of a wait list at registration had no effect on the probability that a semi-urgent or non-urgent patient would undergo CABG through unplanned emergency admission before or during a certain wait-list week.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".