Investigation of a retroesophagoscopic approach to nasopharyngoscopy as an alternative to the conventional retroflexed endoscopic approach for selected indications in feline cadavers and client-owned cats
Bibliographic record
Abstract
OBJECTIVE: To describe a retroesophagoscopic approach (ROSA) to nasopharyngoscopy and compare it with the conventional retroflexed endoscopic approach (REA). ANIMALS: 36 feline cadavers and 2 client-owned cats with nasopharyngeal disorders. PROCEDURES: 36 veterinarians participated in the experimental portion of the study involving feline cadavers. Each veterinarian performed the ROSA and REA to nasopharyngoscopy on a feline cadaver once, attempting to identify and biopsy 2 landmarks (soft palate and choanae) with each approach while time was recorded. Numeric scales were used to measure perceived ease of use and image quality for both techniques. Data were compared between approaches by an independent statistician. The ROSA approach was also used as part of the diagnostic workup for the 2 client-owned cats. RESULTS: 35 of the 36 (97%) veterinarians were able to identify and biopsy both landmarks using the ROSA, whereas 21 (58%) veterinarians were able to visualize both landmarks using the REA and 19 (53%) successfully biopsied the landmarks. Image quality for the soft palate was scored higher with the ROSA (median score, 7.5/10) than with the REA (4.5/10). The ROSA was fast and easy to perform. This approach was also successfully performed in the 2 client-owned cats with nasopharyngeal disorders, with no complications reported. CONCLUSIONS AND CLINICAL RELEVANCE: The ROSA was found to be a fast, effective, and easy alternative endoscopic technique for assessment of the nasopharynx in cats. This approach may allow use of various instruments that could be relevant for interventional procedures. However, the ROSA was also invasive and should be considered for diagnostic and therapeutic purposes for selected indications only when REA is unsuccessful.
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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 | 0.003 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".