Hyoid Bone Syndrome and Dysphonia: Can Throat Pain Affect the Voice?
Bibliographic record
Abstract
OBJECTIVES/HYPOTHESIS: To investigate the relationship of throat pain and dysphonia. STUDY DESIGN: Prospective cohort study. METHODS: Forty-five subjects presenting with hyoid bone syndrome (HBS) and dysphonia were asked to rate their pain on a numerical rating scale and complete the 10-item Voice-Related Quality of Life (V-RQOL) questionnaire prior to and at 1-week follow-up after treatment with triamcinolone injection into the attachments to the affected greater cornu(s). Wilcoxon signed-rank tests were applied to evaluate if the overall V-RQOL scores, the physical functioning (PF) and social-emotional (SE) domain scores, and pain scores changed significantly after treatment. To evaluate how change in perceived pain affected V-RQOL, the differences in the V-RQOL, PF, and SE domain scores, and in pain scores were calculated for each subject. Three linear models were fit to the response variables, ΔV-RQOL, ΔPF, and ΔSE, using ΔPain as a predicting variable. RESULTS: V-RQOL, PF, and SE domain scores, and pain scores all improved significantly with treatment. A bigger decrease in the pain score led to a bigger increase in V-RQOL and domain scores, with slopes varying between -1.1 and -1.4. The PF domain scores showed the greatest improvement with decrease in pain scores. CONCLUSIONS: Effective treatment of HBS led to improvement in patients' voice complaints, suggesting that throat pain may have a direct effect on voice. This may be related to compensatory perilaryngeal adjustments patients make when speaking with a "guarding" effect when they have throat pain. LEVEL OF EVIDENCE: IV (Cohort study) Laryngoscope, 131:E2303-E2308, 2021.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".