Examining the Effect of Time-From-Treatment on Activities of Daily Living Kinematics in Breast Cancer Survivors
Bibliographic record
Abstract
Breast cancer affects one in 8 females with a 5-year survival rate of 89%. Up to 72% of breast cancer survivors have trouble with activities of daily living (ADL) following treatment. Increased time-from-treatment improves some measures of function, yet ADL limitations persist. Therefore, this study assessed the effect of time-from-treatment on upper extremity kinematics during ADLs in breast cancer survivors. Twenty-nine female breast cancer survivors were divided into 2 groups: <1 year (n = 12) and 1-2 years (n = 17) from treatment. Kinematics were collected during 6 ADL tasks, and humerothoracic joint angles were quantified. A 2-way mixed analysis of variance assessed the effects of time-from-treatment and arm on maximum angles for each ADL. Decreased maximum angle existed for breast cancer survivors with increased time-from-treatment during all ADLs. Breast cancer survivors in the 1-2 years group used ∼28° to 32° lower elevation, ∼14° to 28° lower axial rotation, and ∼10° to 14° lower plane of elevation range across tasks. Decreased ranges of arm movement during ADLs with increased time-from-treatment may reflect compensatory movement strategies. Recognizing this shift in strategies and accompanying underlying disease progression can help inform responses to functional performance limitations in breast cancer survivors as delayed effects are present posttreatment.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".