A Toolbox of Surgical Techniques for Palatal Fistula Repair
Bibliographic record
Abstract
OBJECTIVE: To provide an inventory of oronasal fistula repair techniques alongside expert commentary on which techniques are appropriate for each fistula type. DESIGN: A 4-stage approach was used to develop a consensus on surgical techniques available for fistula repair: (1) in-person discussion of oronasal fistula cases among cleft surgeons, (2) development of a schema for fistula management using transcripts of the in-person case discussion, (3) evaluation of the preliminary schema via a web-based survey of additional cleft surgeons, and (4) revision of the management schema using survey responses. PARTICIPANTS: Six cleft surgeons participated in the in-person case discussion. Eleven additional surgeons participated in the web-based survey. Participants had diverse training experiences, having completed residency and fellowship at 20 different hospitals. RESULTS: A schema for fistula management was developed, organized by fistula location. The schema catalogues all viable approaches for each location. For fistulae involving the soft palate, the schema stresses the importance of evaluating for velopharyngeal insufficiency (VPI) and incorporating VPI management into fistula repair. For fistulae involving the hard palate, the schema separately enumerates the techniques available for nasal lining repair and for oral lining repair in each region. The schema also catalogues the diversity of approaches to lingual- and labioalveolar fistula, including variation in timing, orthodontic preparation, and simultaneous alveolar bone grafting. CONCLUSIONS: This study employed consensus methods to create a comprehensive inventory of available fistula repair techniques and to identify preferential techniques among a diverse group of surgeons.
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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 | 0.016 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".