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Record W2769773000 · doi:10.1016/j.jses.2017.09.001

The effect of lipid levels on patient-reported outcomes in patients with rotator cuff tears

2017· article· en· W2769773000 on OpenAlexaboutno aff
Jianyu Lai, Christopher Robbins, Bruce S. Miller, Joel Gagnier

Bibliographic record

VenueJSES Open Access · 2017
Typearticle
Languageen
FieldMedicine
TopicShoulder Injury and Treatment
Canadian institutionsnot available
FundersUniversity of MichiganVanderbilt University
KeywordsMedicineRotator cuffVisual analogue scaleDyslipidemiaPhysical therapyElbowInternal medicineTearsPatient satisfactionSurgery

Abstract

fetched live from OpenAlex

BackgroundLipid disorders could be associated with the prevalence and outcomes of rotator cuff diseases. This study aimed to learn how levels of various types of lipids influence the patient-reported outcomes of patients with rotator cuff tears (RCTs).MethodsData from a cohort study of 135 patients with RCTs were used. The outcome measures included Western Ontario Rotator Cuff (WORC) index, American Shoulder and Elbow Surgeons (ASES) standardized shoulder assessment form, Single Assessment Numeric Evaluation, visual analog scale for pain and satisfaction, and Veterans RAND 12-Item Health Survey (VR-12). Multivariable random-effects models were built to examine how total cholesterol, triglycerides, high-density lipoprotein (HDL), low-density lipoprotein, and ratio of total cholesterol to HDL influence each outcome, controlling for covariates.ResultsAfter adjusting for age, gender, surgery, smoking, and baseline outcome values, patients with triglycerides >150 mg/dL had significantly higher pain visual analog scale (β = 5.86; P = .017) and lower VR-12 physical component summary (β = −2.71; P = .002) scores. Patients with low HDL had significantly worse WORC (β = 132.26; P = .020) and ASES (β = −7.05; P = .005) scores, more pain (β = 6.69; P = .024), and less satisfaction (β = −6.53; P = .008). The ratio of total cholesterol to HDL was associated with worse WORC (β = 58.46; P = .006) and ASES scores (β = −2.74; P = .002), more pain (β = 4.49; P < .001), and worse VR-12 physical component summary score (β = −1.03; P = .017).ConclusionsDyslipidemia may decrease the improvement of patient-reported outcomes in patients undergoing treatment for RCTs; high triglycerides and low HDL may have the most impact. Lipid disorders could be associated with the prevalence and outcomes of rotator cuff diseases. This study aimed to learn how levels of various types of lipids influence the patient-reported outcomes of patients with rotator cuff tears (RCTs). Data from a cohort study of 135 patients with RCTs were used. The outcome measures included Western Ontario Rotator Cuff (WORC) index, American Shoulder and Elbow Surgeons (ASES) standardized shoulder assessment form, Single Assessment Numeric Evaluation, visual analog scale for pain and satisfaction, and Veterans RAND 12-Item Health Survey (VR-12). Multivariable random-effects models were built to examine how total cholesterol, triglycerides, high-density lipoprotein (HDL), low-density lipoprotein, and ratio of total cholesterol to HDL influence each outcome, controlling for covariates. After adjusting for age, gender, surgery, smoking, and baseline outcome values, patients with triglycerides >150 mg/dL had significantly higher pain visual analog scale (β = 5.86; P = .017) and lower VR-12 physical component summary (β = −2.71; P = .002) scores. Patients with low HDL had significantly worse WORC (β = 132.26; P = .020) and ASES (β = −7.05; P = .005) scores, more pain (β = 6.69; P = .024), and less satisfaction (β = −6.53; P = .008). The ratio of total cholesterol to HDL was associated with worse WORC (β = 58.46; P = .006) and ASES scores (β = −2.74; P = .002), more pain (β = 4.49; P < .001), and worse VR-12 physical component summary score (β = −1.03; P = .017). Dyslipidemia may decrease the improvement of patient-reported outcomes in patients undergoing treatment for RCTs; high triglycerides and low HDL may have the most impact.

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 score0.023
Threshold uncertainty score0.315

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 communication0.0000.000
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 head0.050
GPT teacher head0.415
Teacher spread0.365 · 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

Citations5
Published2017
Admission routes1
Has abstractyes

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