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Record W2615372101 · doi:10.1016/j.jses.2017.03.003

Impact of rotator cuff tendon reparability on patient satisfaction

2017· article· en· W2615372101 on OpenAlexaffabout
Helen Razmjou, Richard Holtby

Bibliographic record

VenueJSES Open Access · 2017
Typearticle
Languageen
FieldMedicine
TopicShoulder Injury and Treatment
Canadian institutionsHealth Sciences CentreUniversity of TorontoSunnybrook Health Science Centre
Fundersnot available
KeywordsRotator cuffMedicinePatient satisfactionTendonSurgery

Abstract

fetched live from OpenAlex

Background The primary purpose of this study was to explore the relationship between patient satisfaction and rotator cuff tendon reparability. Materials and methods This was a secondary analysis of prospectively collected data of consecutive patients who underwent arthroscopic repair of full-thickness rotator cuff tear and were followed up for 2 years. The satisfaction level was rated on a 6-point Likert scale. Patient-oriented disability measures included the American Shoulder and Elbow Surgeons score, the short version of the Western Ontario Rotator Cuff index, the Constant-Murley score, and the Quick Disabilities of the Arm, Shoulder, and Hand. Partial repair was defined as repair with >1 cm residual gap. Results There were 145 patients (65 women, 80 men; mean age, 62 years) who met the inclusion criteria. There were 12 massive, 31 large, and 102 small or moderate rotator cuff tears. Of 43 large or massive tears, 23 had a partial repair. There was a statistically significant relationship between satisfaction and tendon reparability ( P = .01). Patients with work-related shoulder injury reported less satisfaction with surgery ( P = .005). Age, gender, or tear size did not affect satisfaction with surgery. Satisfaction was a predictor of all postoperative outcome scores after being adjusted for preoperative scores ( P = .001 to P < .0001). Conclusion In this study, patients with partial repair and those with an active compensable injury were less satisfied with surgery than their counterparts were. Older age, female sex, or a larger tear was not a negative predictor of patient satisfaction.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.067
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.109
GPT teacher head0.502
Teacher spread0.393 · 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

Citations9
Published2017
Admission routes2
Has abstractyes

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