What Factors Contribute to the Delayed Transfers in the Post Anesthetic Care Unit (PACU) at Sunnybrook Health Sciences Centre?
Bibliographic record
Abstract
<p>Purpose: The aim of this study is to find the main factors (medical or non-medical) causing PACU delays at Sunnybrook Health Sciences Centre using 6-months of data from Sunnybrook’s TrackOR system.</p> <p>Methods: The dataset collected consisted of 8,391 medical records of surgeries conducted out of which 2,306 patients faced transfer delays, meaning the patients did not leave PACU within 5 minutes of being ready for discharge. Therefore, 2,306 data points were used for the analysis which contained a reason for delayed discharge from PACU. Multivariate linear models are used in the analysis.</p> <p>Findings: The most common reasons for delays were porter problems (33.8%; n = 735) followed by assigned bed not available on time (20.4%; n = 444) and no bed on unit (15.1%, n = 327). These 3 administrative reasons account for 69.3% of PACU delays.</p>
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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".