Outcomes and prognostic factors of surgical treatments for brachycephalic obstructive airway syndrome in 3 breeds
Bibliographic record
Abstract
OBJECTIVE: To determine prognostic indicators for the surgical treatment of brachycephalic obstructive airway syndrome (BOAS) and to compare the prognosis of 2 multilevel surgical procedures. STUDY DESIGN: Prospective clinical study. SAMPLE POPULATION: Client-owned pugs, French bulldogs, and bulldogs (n = 50). METHODS: Noninvasive whole-body barometric plethysmography (WBBP) was used to assess respiratory function before, 1 month and 6 months after upper airway corrective surgery. Postoperatively, BOAS indices (ie, ascending severity score generated from WBBP data, 0%-100%) that equaled to or exceeded the cut-off values of BOAS in the diagnostic models were considered to have a "poor prognosis." A multivariate logistic regression was used to assess predictors for prognosis. RESULTS: The median BOAS indices decreased after surgery (from 76% to 63%, P < .0001), although dogs with indices in this range would still be considered clinically affected. Age (odds ratios [OR] = 0.96, 95% confidence interval [CI]: 0.93-0.99, P < .05), body condition (OR = 0.06, 95% CI: 0.01-0.39, P < .01), laryngeal collapse (OR = 6.1, 95% CI: 1-37.22, P < .05), and surgical techniques (OR = 7.94, 95% CI: 1.17-54.01, P < .05) were associated with postoperative prognosis. The multivariate model suggests modified multilevel surgery (MMS) may have a better outcome than traditional multilevel surgery (TMS) (P = .034). The positive predictive value of the logistic model was 84% (95% CI: 68-94%) and the area under the receiver operating characteristic (ROC) curve was 89% (95% CI: 78-99%, P <.0001). CONCLUSIONS: Younger age, normal body condition, presence of laryngeal collapse, and treatment with TMS were negative prognostic factors after surgical treatment of BOAS. MMS is recommended, particularly in dogs with a higher probability of poor prognosis.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".