High resolution analysis of wait times and factors affecting surgical expediency
Bibliographic record
Abstract
Objectives: Wait times in Canada are increasingly being monitoredas an indicator of quality health care delivery. We created a higherresolution picture of the wait experienced by urological surgerypatients beginning with the initial referral. In doing so, we hopedto (a) identify potential bottlenecks and common delays at ourcentre, and (b) identify predictors of wait time.Methods: The charts of 322 patients undergoing surgery fromNovember 2007 to March 2008 were reviewed and specific dates,patient factors and delays were recorded. The data were used todetail the patient’s wait and to determine the patient factors whichwere predictive of wait time.Results: The mean time from decision to operate to the day ofoperation was 75.87 days for all patients. This number accountsfor 53% of the wait time, while the time from referral to decision tooperate is 47%. Predictors of a decreased wait time include cancercases, younger age, urgency score, repeat patients and female genderin multivariate analysis. Delays were experienced by 16.8% ofpatients; most common delays were operating room cancellations/time constraints, patients requiring further optimization and delaysin referral (4.7%, 3.4% and 3.1%, respectively).Conclusions: The waiting process is complex; the actual waitingtime that a patient must endure is much longer than the wait timestraditionally recorded and reported by hospitals. As strategies areimplemented to decrease wait times, it will become increasinglyimportant to monitor the entire wait time from referral to operationand to ensure that changes are being made that truly decreasewait times and not simply shift where and when the patient waits.
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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 | 0.001 | 0.001 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".