Post-operative outcomes after cleft palate repair in syndromic and non-syndromic children: a systematic review protocol
Bibliographic record
Abstract
BACKGROUND: There is a lack of high-level evidence on the surgical management of cleft palate. An appreciation of the differences in the complication rates between different surgical techniques and timing of repair is essential in optimizing cleft palate management. METHOD: A comprehensive electronic database search will be conducted on the complication rates associated with cleft palate repair using MEDLINE, EMBASE, and the Cochrane Central Register of Controlled Trials. Two independent reviewers with expertise in cleft pathology will screen all appropriate titles, abstracts, and full-text publications prior to deciding whether each meet the predetermined inclusion criteria. The study findings will be tabulated and summarized. The primary outcomes will be the rate of palatal fistula, the incidence and severity of velopharyngeal insufficiency, and the rate of maxillary hypoplasia with different techniques and also the timing of the repair. A meta-analysis will be conducted using a random effects model. DISCUSSION: The evidence behind the optimal surgical approach to cleft palate repair is minimal, with no gold standard technique identified to date for a certain type of cleft palate. It is essential to appreciate how the complication rates differ between each surgical technique and each time point of repair, in order to optimize the management of these patients. A more critical evaluation of the outcomes of different cleft palate repair methods may also provide insight into more effective surgical approaches for different types of cleft palates.
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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 | 0.033 | 0.037 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.017 | 0.014 |
| Bibliometrics | 0.013 | 0.009 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.039 | 0.003 |
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".