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Trends in Open Versus Endoscopic Carpal Tunnel Release From 2010 to 2021

2024· article· en· W4399725962 on OpenAlexaff
Philip P. Ratnasamy, Katelyn E. Rudisill, Peter Y. Joo, Lisa Lattanza, Jonathan N. Grauer

Bibliographic record

VenueJAAOS Global Research and Reviews · 2024
Typearticle
Languageen
FieldMedicine
TopicPeripheral Nerve Disorders
Canadian institutionsObject Research Systems (Canada)
FundersNational Center for Advancing Translational SciencesNational Heart, Lung, and Blood Institute
KeywordsEndoscopic carpal tunnel releaseCarpal tunnel syndromeCarpal tunnel releaseMedicineSurgery

Abstract

fetched live from OpenAlex

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.746
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.001

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.

Opus teacher head0.200
GPT teacher head0.499
Teacher spread0.300 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations3
Published2024
Admission routes1
Has abstractyes

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