A Qualitative Study of Rehabilitation Professionals' Practices to Define the Presence of Arm Morbidity After Breast Cancer Surgery
Bibliographic record
Abstract
Background: Rehabilitation professionals (RPs) play a major role in identifying, managing, and treating upper-body issues in individuals following breast cancer surgery. Varying definitions of postoperative arm morbidity in the literature have hampered development of standardized surveillance programs for people undergoing breast cancer surgery within clinical care. Our objective was to explore RPs' practices in defining the presence of arm morbidity after breast cancer surgery. Methods: This qualitative study used semistructured focus group interviews with 29 RPs from 5 health authorities in British Columbia, Canada. Transcripts were analyzed using content analysis. Results: Two categories captured RPs' overarching lack of consensus in defining the presence of postoperative arm morbidity: (1) Complex concerns, complex considerations; and (2) Many ways of measuring arm morbidity. Varying perspectives exist as to which upper-body issues and functional criteria constitute arm morbidity, as well as which characteristics to consider in identifying who is at risk of developing arm morbidity. In tandem, there is currently no gold standard outcome measure or standardized assessment to identify arm morbidity. Conclusion: Because of the complex interaction between different breast cancer treatments and various environmental and personal factors, there is currently a lack of consensus among RPs about how to define and assess arm morbidity. Our findings demonstrate the presence of arm morbidity is challenging to characterize, given its multifaceted presentation, inconsistent approaches to risk stratification across clinical settings and geographical regions the RPs worked, and numerous ways of measuring arm morbidity.
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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 | 0.021 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.011 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| 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".