Improving Evidence-Based Methods of Characterizing Shoulder-Related Quality of Life for Survivors of Breast Cancer
Bibliographic record
Abstract
Background: Breast cancer is prevalent among Canadian women, but treatments may cause functional impairments among survivors. Despite a substantial number of survivors joining the population yearly, minimal research has approached the challenges faced by this population after primary treatment. The purpose of this study was to classify the different function of survivors of breast cancer and determine factors that differed across groups of survivors. Methods: Thirty-five survivors of breast cancer within 2 years since the conclusion of their treatment participated in this cross-sectional study. Participants completed quality-of-life questionnaires, followed by a full-body dual-energy x-ray absorptiometry scanning. The collection concluded with maximal force exertions at the shoulder and maximum shoulder range of motion. Results: This study determined, through feature reduction, that internal rotation force production, active extension range of motion, and 3 shoulder-related quality-of-life variables (energy/fatigue, social functioning, and pain) separated survivors within 2 years of treatment into 2 clusters (low- and high-score clusters [LSC/HSC], respectively). The LSC participants had higher self-reported disability, lower shoulder-related quality of life, force production, and flexion range of motion. Conclusion: Clustering survivors of breast cancer allows for a better understanding of deficits experienced by some individuals, as well as brings awareness to factors to monitor, and address in rehabilitation efforts.
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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 | 0.081 | 0.181 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.010 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".