A patient-centred approach toward surgical wait times for colon cancer: a population-based analysis
Bibliographic record
Abstract
BACKGROUND: Administrative wait times reflect the time from the decision to treat until surgery; however, this does not reflect the total time a patient actually waits for treatment. Several factors may prolong the wait for colon cancer surgery. We sought to analyze the time from the date of surgical consultation to the date of surgery and any events within this time frame that may extend wait times. METHODS: We retrospectively reviewed the cases of all adult patients in Ontario aged 18-80 years with diagnosed colon cancer who did not receive neoadjuvant therapy and underwent resection electively between Jan. 1, 2002, and Dec. 31, 2009. Wait times were measured from the date of surgical consultation to the date of surgery. We chose a wait time of 28 days, reflecting local administrative targets, as a comparative benchmark. We performed univariate and multivariate analyses to identify variables contributing to a waits longer than 28 days. Variables were analyzed in continuous linear and logistic regression models. RESULTS: We included 10 223 patients in our study. The median wait time from initial surgical consultation to resection was 31 (range 0-182) days. Age older than 65 years had a negative impact on wait time. Preoperative services, including computed tomography, cardiac consultation, echocardiography, multigated acquisition scan, magnetic resonance imaging, colonoscopy and cardiac catheterization also significantly increased wait times. Wait times were longer in rural hospitals. CONCLUSION: Preoperative services significantly increased wait times between initial surgical consultation and surgery.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".