Shoulder Dysfunction in Breast Cancer Survivors: Can Treatment Type or Musculoskeletal Factors Identify Those at Higher Risk?
Bibliographic record
Abstract
Background and Objective: Breast cancer is the most commonly diagnosed cancer in Canadian women. Breast cancer survivors are known to experience shoulder dysfunction, but the influence of musculoskeletal and treatment factors has yet to be investigated in a Saskatchewan population, which was the purpose of this study. Methods: Two study designs were used to assess risk factors for dysfunction: (1) a cross-sectional Web-based questionnaire and (2) prospective cohort analysis of preoperative musculoskeletal assessment combined with postoperative Shoulder Pain and Disability Index (SPADI) score. Data from the survey were summarized and analyzed using χ2 tests (P < .05), while nonparametric measures were used to calculate temporal differences and associations between musculoskeletal risk factors and disability. Results: Commonly reported shoulder problems after treatment were stiffness (63.5%), restricted range of motion (61.9%), and changes in arm/hand sensation (61.9%). Axillary lymph node dissection and radiation therapy were associated with more shoulder problems than other treatment types. SPADI scores increased by an average of 8.1% from baseline to 3 months postsurgery. A clinically significant 18% increase between these time points was moderately associated with a history of shoulder problems and restricted humeral extension preoperatively (average = 37.7° vs 48.9°). Conclusions: Breast cancer survivors from Saskatchewan have a high prevalence of shoulder problems following treatment. Clinically significant impairments in shoulder function are associated with select treatment types and preoperative impairments. These results can be used to identify high-risk patients before cancer treatment and direct their rehabilitation.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".