Medialization laryngoplasty/arytenoid adduction: U.S. outcomes, discharge status, and utilization trends
Bibliographic record
Abstract
OBJECTIVES/HYPOTHESIS: To evaluate trends, outcomes, and healthcare utilization following medialization laryngoplasty (ML) with or without arytenoid adduction (AA) over 10 years. STUDY DESIGN: Retrospective observational study. METHODS: Using OptumLabs Data Warehouse, trends, outcomes, and healthcare utilization from 2006 to 2015 were examined with a focus on discharge type (same day or not). Predictors of postoperative emergency department (ED) use and hospitalization were determined by multivariable logistic regression. RESULTS: Overall rate of ML was 1.09 per 100 thousand enrollees per year. Of these, 7.8% ML were combined with an AA. Outpatient same-day discharge represented 62.0% (1,142 of 1,843) of total patients, steadily increasing over the 10-year period (P < 0.01). There was a 5.9% revision ML rate and 1.0% rate of tracheotomy within 1 day of ML. A total of 5.6% visited an ED, and 5.4% were admitted to a hospital following initial discharge within 30 days. Same-day discharge was found to be a predictor of hospitalization within 30 days after ML (odds ratio [OR] 1.74, P = 0.0452), along with Elixhauser comorbidity index of 4 + (OR 5.74, P = 0.0001). Pulmonary embolism, pulmonary hypertension, and weight loss were top predictors of ED visit or hospitalization. CONCLUSION: To our knowledge, this is the first search evaluating national claims data for ML with or without AA. Overall rate of ML is low, and same-day discharge has become more common over a 10-year period, with an associated higher 30-day hospital admission risk. Correct patient selection criteria for disposition status cannot be fully determined based on current data, but a high Elixhauser comorbidity index clearly carries increased risk for hospitalization after initial discharge. LEVEL OF EVIDENCE: 4 Laryngoscope, 129:952-960, 2019.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".