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Record W2470615941 · doi:10.1007/s00167-016-4228-5

Preoperative and post-operative sleep quality evaluation in rotator cuff tear patients

2016· article· en· W2470615941 on OpenAlexaboutno aff
Sancar Serbest, Uğur Tiftikçi, Aydogan Askın, Ferda Yaman, Murat Alpua

Bibliographic record

VenueKnee Surgery Sports Traumatology Arthroscopy · 2016
Typearticle
Languageen
FieldPsychology
TopicSleep and related disorders
Canadian institutionsnot available
Fundersnot available
KeywordsRotator cuffMedicineSurgeryPittsburgh Sleep Quality IndexQuality of life (healthcare)CuffRotator cuff injuryPhysical therapySleep quality

Abstract

fetched live from OpenAlex

PURPOSE: The aim of this study was to examine the potential relationship between subjective sleep quality and degree of pain in patients with rotator cuff repair. METHODS: Thirty-one patients who underwent rotator cuff repair prospectively completed the Pittsburgh Sleep Quality Index, the Western Ontario Rotator Cuff Index, and the Constant and Murley shoulder scores before surgery and at 6 months after surgery. Preoperative demographic, clinical, and radiologic parameters were also evaluated. RESULTS: The study analysed 31 patients with a median age of 61 years. There was a significant difference preoperatively versus post-operatively in terms of all PSQI global scores and subdivisions (p < 0.001). A statistically significant improvement was determined by the Western Ontario Rotator Cuff Scale and the Constant and Murley shoulder scores (p ˂ 0.001). CONCLUSION: Sleep disorders are commonly seen in patients with rotator cuff tear, and after repair, there is an increase in the quality of sleep with a parallel improvement in shoulder functions. However, no statistically significant correlation was determined between arthroscopic procedures and the size of the tear and sleep quality. It is suggested that rotator cuff tear repair improves the quality of sleep and the quality of life. LEVEL OF EVIDENCE: IV.

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), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.042
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.000

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 head0.019
GPT teacher head0.320
Teacher spread0.301 · 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 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

Citations59
Published2016
Admission routes1
Has abstractyes

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