Swelling, Numbness, Pain, and Their Relationship to Arm Function Among Breast Cancer Survivors: A Disablement Process Model Perspective
Bibliographic record
Abstract
The disablement process model proposed by Nagi in 1965 and subsequently expanded by Verbrugge and Jette was used heuristically to study the relationships among morbidities and arm/shoulder function limitations that breast cancer survivors experience in the period following treatment. A telephone survey was administered to 148 patients (67%) from among 222 breast cancer survivors who had undergone surgery in 1997 and 1998. Sixty-three percent of respondents reported experiencing some numbness, while 35% noticed swelling. Between 13% and 15% reported moderate to severe pain. Similar proportions said the occurrence of pain ranged from intermittent to constant. Between 1% and 4% reported problems with shoulder abduction and flexion and a decrease in arm strength and daily use of the arm. Swellings in the torso and arm tended to cluster into two different factors. Numbness followed a similar pattern. Apart from numbness in the arm, all the other factors had strong significant associations with one another. In multiple regression analyses, current pain intensity and swelling in the arm were independently related to current functional status of the arm/shoulder. The results suggest that it may be feasible to use patients' self-reports to develop a simple lymphedema-specific tool to monitor the functional status of women living with or at risk for lymphedema. Such a tool, if properly designed and implemented, would allow for the timely introduction of lymphedema or pain management strategies to improve arm function and ultimately the quality of life of breast cancer survivors.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".