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Record W4225744853 · doi:10.1177/17585732221092016

Comparing surgical outcomes of anterior capsular release vs circumferential release for persistent capsular stiffness

2022· review· en· W4225744853 on OpenAlexaff
Safiya Erdogan, Seaher Sakha, Ajaykumar Shanmugaraj, Carlos Prada, Rachel M. Frank, Timothy Leroux, Moin Khan

Bibliographic record

VenueShoulder & Elbow · 2022
Typereview
Languageen
FieldMedicine
TopicShoulder Injury and Treatment
Canadian institutionsImpactUniversity of TorontoMcMaster University
Fundersnot available
KeywordsMedicineComplicationRandomized controlled trialSurgeryConcomitantCapsulitisRange of motionCochrane Library

Abstract

fetched live from OpenAlex

Purpose: To consolidate the existing literature evaluating anterior capsular release and circumferential capsular release in the treatment of adhesive capsulitis (AC) of the shoulder. Methods: The electronic databases PUBMED, EMBASE, MEDLINE and CENTRAL (Cochrane Central Register of Controlled Trials) were searched from data inception to October 8, 2020. Data are presented descriptively where appropriate. A meta-analysis was conducted for patient-reported outcomes. Results: Overall, there were forty-six articles included. The majority of patients underwent circumferential release compared to anterior release (80.1% vs. 19.9%). Concomitant Manipulation Under Anesthesia (MUA) was employed in 25 studies, with a higher occurrence in the anterior compared to the circumferential release group (70% vs 60%). Both groups experienced significant improvements postoperatively in range of motion (ROM) and patient-reported outcomes. Complication rates were low for both anterior release (0.67%) and 360° release (0.44%). Conclusion: Both anterior and circumferential release are effective techniques for treating AC with low complication rates. Future studies should improve documentation of patient demographics, surgical techniques and outcomes to determine an individualized treatment protocol for patients. Level of evidence: Level IV, Systematic Review of Level I-IV studies.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.959
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0060.004
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.108
GPT teacher head0.378
Teacher spread0.270 · 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.

Study designNot applicable
Domainnot available
GenreReview

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

Citations5
Published2022
Admission routes1
Has abstractyes

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