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Record W2949873449 · doi:10.1177/1758573219852977

Decreased complication profile and improved clinical outcomes of primary reverse total shoulder arthroplasty after 2010: A systematic review

2019· review· en· W2949873449 on OpenAlexaff
Raphael J. Crum, Darren de, Favian Su, Bryson P. Lesniak, Albert Lin

Bibliographic record

VenueShoulder & Elbow · 2019
Typereview
Languageen
FieldMedicine
TopicShoulder Injury and Treatment
Canadian institutionsMcMaster Children's Hospital
FundersNational Institute of General Medical Sciences
KeywordsMedicineComplicationPerioperativeArthroplastySurgeryElbowImplant

Abstract

fetched live from OpenAlex

The purpose of this review was to update the complication profile of reverse total shoulder arthroplasty (rTSA) post-2010, given greater procedural familiarity, improved learning curves, enhanced implant designs, and increased attention to the nuances of patient selection. Three electronic databases were searched and screened in duplicate from 1 January 2010 to 16 December 2018 based on predetermined criteria. Twenty-two studies examining 1455 patients (26% male; mean age: 73.4 ± 3.6; mean follow-up: 23.4 ± 14.3 months) were reviewed. Post-operative motion ranged a mean 122.4° ± 11.5° flexion, 109° ± 19.4° abduction, and 33° ± 11.2°/41° ± 5° external/internal rotation. Post-operative mean Constant score was 58.9 ± 10.1, American Shoulder Elbow Surgeon score was 73.4 ± 6.1, Simple Shoulder Test score was 63.5 ± 6.5, and a Visual Analog Scale pain score was 1.6 ± 0.9. The overall complication rate was 18.2% and major complication rate was 15.4%. Compared to pre-2010, the overall complication rate of 18.2% is lower than previous rates of 19%-68%, with the rate of "major" complications dropping three-fold from 15.4% to 4.6%. The data suggest that rTSA is a safe and efficacious alternative to aTSA and HA, and the "stale" nature of previous complication profiles are points fundamental to perioperative discussions surrounding rTSA.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.158
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0090.002
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
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 head0.078
GPT teacher head0.395
Teacher spread0.317 · 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 designSystematic review
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

Citations12
Published2019
Admission routes1
Has abstractyes

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