Folded-flap palatoplasty and traditional staphylectomy yield similar postoperative soft palate geometry in French Bulldogs undergoing airway surgery
Bibliographic record
Abstract
Objective: To compare CT-derived anatomical geometry of the soft palate and clinical outcome following folded-flap palatoplasty (FFP) or traditional staphylectomy (TS). Methods: Client-owned French Bulldogs were prospectively enrolled between July 2021 and September 2024. An exercise tolerance test (ETT) and head CT were performed preoperatively and 3 months postoperatively. Dogs were randomized to receive FFP or TS in conjunction with modified multilevel surgery for brachycephalic obstructive airway syndrome (BOAS). Computed tomography measurements were performed and compared between groups. The length of the soft palate was measured from the end of the hard palate to the caudal tip of the soft palate. Soft palate thickness was measured by dividing the length into equal thirds, and measurements were taken of the rostral, middle, and caudal thirds. Results: Eighteen dogs completed the study (FFP, n = 7; TS, 11). With the use of either surgical technique, soft palates were significantly shorter and significantly thinner postoperatively at the rostral and middle thirds but not at the caudal third when compared to preoperative measurements. Surgical technique was not shown to have a significant effect on the change in palatal length or thickness. There was no significant difference in improvement in ETT scores between the FFP and TS groups. Conclusions: FFP did not result in a significantly thinner or shorter palate or greater improvement in ETT score when compared to TS. Both techniques resulted in significantly shorter and thinner palates postoperatively. Clinical Relevance: Equivalent clinical outcome can be expected following FFP or TS combined with modified multilevel surgical techniques for the treatment of BOAS.
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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 | 0.001 |
| 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) | 0.001 | 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".