Waiting for children’s surgery in Canada: the Canadian Paediatric Surgical Wait Times project
Bibliographic record
Abstract
BACKGROUND: In addition to possibly prolonged suffering and anxiety, extended waits for children's surgery beyond critical developmental periods has potential for lifelong impact. The goal of this study was to determine the duration of waits for surgery for children and youth at Canadian paediatric academic health sciences centres using clinically-derived access targets (i.e., the maximum acceptable waiting periods for completion of specific types of surgery) as used in this Canadian Paediatric Surgical Wait Times project. METHODS: We prospectively applied standardized wait-time targets for surgery, created by nominal-group consensus expert panels, to pediatric patients at children's health sciences centres across Canada with decision-to-treat dates of Sept. 1, 2007 or later. From Jan. 1 to Dec. 30, 2009, patients' actual wait times were compared with their target wait times to determine the percentage of patients receiving surgery after the target waiting period. RESULTS: Overall, 27% of pediatric patients from across Canada (17,411 of 64,012) received their surgery after their standardized target waiting period. Dentistry, ophthalmology, plastic surgery and cancer surgery showed the highest percentages of surgeries completed past target. INTERPRETATION: Many children wait too long for surgery in Canada. Specific attention is required, in particular, in dentistry, ophthalmology, plastic surgery and cancer care, to address children's wait times for surgery. Improved access may be realized with use of national wait-time targets.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".