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Record W4225120955 · doi:10.53730/ijhs.v6ns1.6431

Limited incision and traditional open carpal tunnel release

2022· article· en· W4225120955 on OpenAlexaboutno aff
L Chethan, Sunil Gaba, Manish Modi

Bibliographic record

VenueInternational Journal of Health Sciences · 2022
Typearticle
Languageen
FieldMedicine
TopicPeripheral Nerve Disorders
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineCarpal tunnel releaseCarpal tunnel syndromeSurgeryCarpal tunnelMedian nerveWristDecompressionAnesthesia

Abstract

fetched live from OpenAlex

Background: Carpal Tunnel Syndrome (CTS) is the most common compression neuropathy of the upper extremity. Even though traditional open release has been considered as standard approach for median nerve decompression, various other techniques are gaining popularity. The aim of this study was to compare the clinical and neurological outcomes between traditional open approach and limited incision approach. Methods: Twenty-eight patients with isolated CTS have included in this study of which four patients had bilateral CTS thus constituting 32 hands (19-right; 13-left). The patients were divided for treatment into two groups, Group A included 21 hands underwent limited incision release, and Group B included 11 hands released by traditional open incision. Patient's were evaluated preoperatively and in six weeks and six months postoperatively, namely, (a) clinical outcomes including Boston carpal tunnel questionnaire (BCTQ), postoperative scar tenderness, hypertrophic scar, pillar pain & quality of scar by Vancouver scar scale (VSS). (b) sensory testing using two-point discrimination (TPD) (c)motor testing using grip and pinch dynamometer, (d) neurological outcome measurement using nerve conduction study (NCS). Results: In each section of BCTQ outcomes, patients in group A showed significant improvement than in group B at both six weeks and six months follow up.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.591
Threshold uncertainty score0.425

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.115
GPT teacher head0.399
Teacher spread0.284 · 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; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations0
Published2022
Admission routes1
Has abstractyes

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