Trends in Open Versus Endoscopic Carpal Tunnel Release From 2010 to 2021
Bibliographic record
Abstract
BACKGROUND: This study compared trends in use, predictive factors, and reimbursement of endoscopic carpal tunnel release (ECTR) withthose of open carpal tunnel release (OCTR) from 2010 to 2021 using a national administrative database. METHODS: ECTR and OCTR patients were identified in the PearlDiver M151Ortho data set. Numeric and proportional utilization of these procedures was characterized for each year of study. Multivariate analysis was conducted to identify predictive factors for having ECTR performed. The average 90-day reimbursement of ECTR and OCTR was determined. RESULTS: From 2010 through 2021, 441,023 ECTR and 1,767,820 OCTR procedures were identified. The proportional use of ECTR compared with OCTR rose from 2010 (15.7% of procedures) to 2021 (26.1%). Independent predictors of having ECTR performed rather than OCTR included geographic variation (compared with having surgery in the Midwest, Northeast odds ratio [OR], 1.53; West OR, 1.62; and South OR, 1.66), having Medicare or commercial insurance (compared with commercial, Medicare OR, 0.94, and Medicaid OR, 0.69), female sex, and fewer comorbidities. The average 90-day reimbursement for ECTR was $3,114.82, compared with $3,087.62 for OCTR. DISCUSSION: As of 2021, over one-fourth of carpal tunnel releases are done endoscopically. Several factors independently predict whether patients receive ECTR or OCTR.
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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".