Measurement of objective shoulder function following breast cancer surgery: a scoping review
Bibliographic record
Abstract
BackgroundShoulder dysfunction is frequently experienced by survivors of breast cancer following surgery, and it is associated with both reduced ability to perform daily living tasks and decreased overall quality of life. Shoulder function is often measured with self-reported questionnaires. It is not known to what extent shoulder function has been objectively measured in order to accurately assess return-to-work related outcomes.ObjectivesThis review aimed to determine what is known about objectively evaluated shoulder function following breast cancer.MethodsA comprehensive search of several databases was performed to identify all relevant research. All identified studies were screened and those including the objective measurement of shoulder function of survivors of breast cancer post-surgery were included. Data were extracted by two reviewers and results were consolidated and presented.ResultsThe majority (71%, n = 175) of studies measured range of motion to assess function of the shoulder in survivors of breast cancer. Range of motion was usually with goniometers (57%, n = 141). Strength was the second most common functional outcome reported. The use of functional work tasks or activities of daily living to assess shoulder ability or capacity was limited to less than 5% (n = 7) of studies. Most studies used baseline measures to assess outcomes (61%, n = 150), while 29% (n = 72) explicitly used the contralateral arm to evaluate dysfunction.ConclusionRange of motion and shoulder strength are frequently measured in survivors of breast cancer. Future investigations should focus on functional movements and outcomes to assess shoulder function, especially for the return-to-work considerations.
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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 | 0.016 | 0.077 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.015 | 0.016 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".