DIAGNOSTIC SENSITIVITY OF SUBJECTIVE AND QUANTITATIVE LARYNGEAL ULTRASONOGRAPHY FOR RECURRENT LARYNGEAL NEUROPATHY IN HORSES
Bibliographic record
Abstract
Recurrent laryngeal neuropathy (RLN) is the most common cause of laryngeal hemiplegia in horses and causes neurogenic atrophy of the intrinsic laryngeal muscles, including the cricoarytenoideus lateralis muscle. Recurrent laryngeal neuropathy results in paresis to paralysis of the vocal fold and arytenoid cartilage, which limits performance through respiratory compromise. Ultrasound has previously been reported to be a useful diagnostic technique in horses with RLN. In this report, the diagnostic sensitivity of subjective and quantitative laryngeal ultrasonography was evaluated in 154 horses presented for poor performance due to suspected upper airway disease. Ultrasonographic parameters recorded were: cricoarytenoideus lateralis echogenicity (subjective and quantitative), cricoarytenoideus lateralis thickness, vocal fold movement, and arytenoid cartilage movement. Ultrasonographic parameters were then compared with laryngeal grades based on resting and exercising upper airway endoscopy. Subjectively increased left cricoarytenoideus lateralis echogenicity yielded a sensitivity of 94.59% and specificity of 94.54% for detecting RLN, based on the reference standard of exercising laryngeal endoscopy. Quantitative left cricoarytenoideus lateralis echogenicity values differed among resting laryngeal grades I-IV. Findings from this study support previously published findings and the utility of subjective and quantitative laryngeal ultrasound as diagnostic tools for horses with poor performance.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".