Surgical wait times for patients with urological cancers: a survey of Canadian surgeons.
Bibliographic record
Abstract
BACKGROUND: The wait times for urological cancer surgeries in Canada has increased over the past 2 decades. This is of concern to patients, physicians and other key stakeholders because there is evidence that delaying surgery beyond a recommended threshold could have a negative impact on clinical outcomes. To address these trends, a Canadian surgical wait times (SWAT) initiative has been undertaken to develop a consensus document and make recommendations on appropriate wait times. As a first step, the SWAT steering committee determined that current wait times estimates were required for the four key disease sites; prostate, bladder, kidney and testes. To obtain such data, a survey of Canadian urological surgeons was undertaken. METHODS: A structured electronic mailing strategy was adopted as recommended by Dillman (1978). Standardized data collection forms were sent to members of the Canadian Urological Association (CUA) and attendees to the 2005 CUA meeting. Survey items consisted of respondent demographic data, information on surgical wait times for the four key disease sites and potential barriers to timely cancer surgery. RESULTS: One hundred and five urological surgeons responded to the survey. There was considerable variation in wait times between and within the four disease sites with bladder and kidney cancer surgeries displaying the widest range. Operating room availability and staging tests were identified as the most significant barriers to efficient cancer surgery. CONCLUSIONS: The wide variation in wait times identified in this study suggest that the overall time to treatment from referral is beyond the duration considered by many experts and by the Canadian Society of Surgical Oncology to be acceptable. These issues need to be addressed through a partnership between the key stakeholders in order to reduce the potentially negative impact on clinical outcomes and patient quality of life.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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".