Anesthetic Techniques for Type-1 (Medialization) Thyroplasty: A Scoping Review
Bibliographic record
Abstract
OBJECTIVE: To explore different anesthesia techniques for medialization thyroplasty and determine how these anesthesia techniques may influence patient safety, patient experience, and surgical outcomes during medialization thyroplasty in adult patients. DATA SOURCES: A comprehensive librarian-designed strategy was used to search EMBASE, MEDLINE, and Web of Science for English language studies from database inception to July 2023. The study was registered on Open Science Framework (10.17605/OSF.IO/R3BV2). REVIEW METHODS: Study selection was independently performed by two investigators for all English language studies of adult patients investigating anesthetic techniques for medialization thyroplasty with a minimum of five patients. Surgical outcomes (voice, perioperative complications, and swallowing), healthcare resource utilization metrics (operating time, length of stay), and patient-reported outcomes measures (PROMs) were analyzed. Study quality was assessed with the Oxford Levels of Evidence tool. RESULTS: From 354 articles, 28 studies were included. The most common anesthetic techniques were combined procedural sedation and local anesthesia (13/28 [46%]), local anesthesia alone (8/28 [29%]), and general anesthesia (GA) (7/28 [25%]). Six studies (21%) reported intraoperative complications (eg, desaturation), and eight (29%) studies reported postoperative complications (eg, airway obstruction). Voice outcomes were assessed in 14 (50%) studies. PROMs, including Voice Handicap Index (3/28 [11%]), were less commonly assessed. Intraoperative fiber-optic visualization was utilized in eight (29%) studies. Only one study assessed swallowing. Only two studies compared outcomes between anesthetic techniques. The median Oxford Level of Evidence was 4. CONCLUSION: Medialization thyroplasty is performed under local anesthetic alone, with combined procedural sedation, and local anesthetic or with GA, with diverse approaches to airway management and minimal perioperative complications. Future research using standardized outcome measures is warranted due to the current paucity in the literature.
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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 | 0.006 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.006 |
| Bibliometrics | 0.010 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".