Time Trends and Determinants of Fistula in Cleft Patients at BC Children's Hospital, Canada
Bibliographic record
Abstract
OBJECTIVE: To examine the time trends and determinants of palatal fistula in children with different types of cleft at British Columbia's Children's Hospital between 1995 and 2012. METHODS: A total of 558 medical charts of nonsyndromic patients with cleft lip and palate were eligible for the chart review. The occurrence of primary palatal fistula was assessed at any time throughout the patient's total observation period. Three types of clefts were recorded: unilateral cleft lip and palate (ULCLP), bilateral cleft lip and palate (BLCLP), and isolated cleft palate (ICP). Cleft severity, time period of treatment, type of surgery and surgeon's experience were tested as determinants. RESULTS: Of all 558 patients, 228 had ULCLP, 226 had ICP, and 104 had BLCLP. The combined postoperative palatal fistula rate was 28%. The significant differences in fistula rates related to type of cleft (patients with BLCLP had the highest fistula rates), time period (rates were higher in earlier years than in later years), type of surgery (highest rates were for two-flap palatoplasty), and surgeons with less experience. CONCLUSIONS: Almost one quarter of the patients, developed fistula, and fistula incidence declined after 2009. The higher fistula rates were determined by cleft severity, time period of treatment, type of surgery, and surgeon's experience.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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 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".