Adjustable laryngeal implant for unilateral vocal cord paralysis.
Bibliographic record
Abstract
BACKGROUND: Laryngeal framework surgery has been widely accepted as a definitive treatment for unilateral vocal cord paralysis (UVCP). An adjustable laryngeal implant has been developed for precise medialization of the paralyzed vocal cord. OBJECTIVES: To describe the implant and surgical technique and to evaluate the effect of vocal cord medialization using an adjustable laryngeal implant on the quality of life of patients with UVCP. PATIENTS AND METHODS: Fifty-three patients with UVCP who had undergone medialization with the adjustable laryngeal implant were identified. All patients completed the Voice Handicap Index (VHI) quality of life questionnaire. Preoperative and postoperative scores were compared. RESULTS: Major advantages over other accepted implants are the stability of the implant, excellent biocompatibility, precise medialization with a micrometric screw, and ease of secondary adjustment. Mean VHI score in all handicap domains was significantly improved following vocal fold medialization (p < .01). Although no difference was found in regard to gender, younger patients had higher handicap scores than older patients preoperatively (p < .05). This difference was not present following medialization. CONCLUSIONS: The adjustable laryngeal implant offers many advantages over other techniques. Vocal cord medialization using the adjustable laryngeal implant significantly improves quality of life as measured by the VHI.
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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 | 0.000 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".