Vocal fold injection material does not preclude interpretation of laryngeal electromyography
Bibliographic record
Abstract
INTRODUCTION/AIMS: Temporary vocal fold injection (VFI) is a common treatment for acute and subacute vocal fold paralysis (VFP). Laryngeal electromyography (LEMG) is useful for diagnosing neurogenic causes of VFP. This study evaluated whether the presence of VFI material prevents interpretation of LEMG in patients with acute and subacute VFP. METHODS: Patients with acute and subacute unilateral VFP (onset ≤6 mo) who underwent temporary VFI within 3 mo preceding LEMG were evaluated. A matched control group that did not undergo VFI was also studied. The LEMG team (laryngologist and electromyographer) performed and interpreted LEMG using a pre-specified protocol, including qualitative and quantitative motor unit analysis. RESULTS: Eighteen patients with VFI underwent LEMG successfully with interpretation of spontaneous activity and motor unit recruitment. Fourteen patients were seen in follow-up to determine accuracy of established LEMG prognosis. Seven of seven subjects with poor LEMG prognosis did not recover vocal fold motion. Five of seven subjects with fair LEMG prognosis recovered vocal fold motion. Findings were similar for the control group. DISCUSSION: VFI augmentation material did not prevent interpretation of meaningful LEMG data in patients with acute and subacute VFP, and accurate prognoses of vocal fold motion recovery were established.
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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 | 0.002 | 0.009 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".