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Record W1918402137 · doi:10.1177/2325967115s00157

Does Smoking Affect Treatment Allocation and Outcomes in Patients with Rotator Cuff Tears?

2015· article· en· W1918402137 on OpenAlexaboutno aff
Germanuel L. Landfair, Christopher Robbins, Joel Gagnier, Asheesh Bedi, James E. Carpenter, Bruce S. Miller

Bibliographic record

VenueOrthopaedic Journal of Sports Medicine · 2015
Typearticle
Languageen
FieldMedicine
TopicShoulder Injury and Treatment
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineRotator cuffTearsContext (archaeology)Prospective cohort studyPhysical therapyLogistic regressionSurgeryElbowInternal medicine

Abstract

fetched live from OpenAlex

Objectives: The objectives of this study were (1) to assess the influence of smoking status on treatment allocation (surgical versus non-surgical management) and (2) to compare the short-term functional outcomes of surgical and non-surgical treatment of rotator cuff tears between smokers and non-smokers. Methods: In the context of a prospective pragmatic cohort study we included 196 subjects with known full-thickness rotator cuff tears who were followed prospectively for 48 weeks. The Western Ontario Rotator Cuff Index (WORC), American Shoulder and Elbow Surgeons (ASES) score, and visual analogue pain scores were collected at baseline, 4, 8, 16, 32, and 48 weeks. Multivariate logistic regression was used to determine predictors of treatment allocation. Generalized linear models and t-tests were used to assess the effect of smoking on outcome measures at baseline. Mixed-effects repeated measures regression models were used to assess the effect of smoking on the outcomes after surgical and non-surgical management of rotator cuff tears. Results: The non-smoking group was older than the smoking group (61.4 years vs. 54.2, p=0.0004). Twenty-two percent of the surgical group and 12% of the non-surgical group were smokers. There was no significant difference between smokers and non-smokers in regards to the proportion of patients who were obese, had diabetes, had experienced rotator cuff tear symptoms for more than a year, had utilized physical therapy, had a large RCT, or who had received workers’ compensation. Smoking status was not significantly associated with allocation to surgical versus non-surgical treatment (OR=0.85, p= 0.762). After adjustment for covariates, subjects who smoked reported less favorable baseline adjusted WORC scores (40.9 vs. 54.5, p=0.0008), lower ASES scores (43.0 vs. 59.9, p=0.0001), as well as worse pain scores (59.5 vs. 42.9, p=0.0001). Within the non-surgical management group, smokers reported significantly lower adjusted WORC scores (38.0 vs. 56.8, p=0.0127), lower ASES scores (39.2 vs. 61.6, p=0.0872), and worse pain (61.9 vs. 42.1, p=0.0176) over 48 months. Similarly, in patients who underwent rotator cuff repair, smokers reported significantly lower adjusted WORC scores (31.1 vs. 40.4, p=0.0352), lower ASES scores (37.7 vs. 50.0, p=0.0143), and worse pain scores (63.2 vs. 51.5, p=0.0408) over the 48-week follow-up period. Conclusion: Subjects who smoked reported worse pain and function scores at baseline and over the course of one year, regardless of whether they received surgical or nonsurgical management. Smoking was not a significant predictor of treatment allocation in this cohort. The disparity in reported function and pain in smokers was less pronounced in those who underwent surgical repair than those who received non-surgical management therapy. Further follow up is needed to more clearly elucidate the influence of smoking on the management and outcome of rotator cuff tears.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.008
metaresearch head score (Gemma)0.037
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.008
Threshold uncertainty score0.042

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.037
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.000
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 head0.015
GPT teacher head0.286
Teacher spread0.271 · 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 source (direct Gemma or distilled Codex), 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

Citations2
Published2015
Admission routes1
Has abstractyes

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