BREAST CANCER EDGE TASK FORCE OUTCOMES: Clinical Measures of Pain
Bibliographic record
Abstract
Background: Pain is one of the most commonly reported impairments after breast cancer treatment affecting anywhere from 16% to 73% of breast cancer survivors. Despite the high reported incidence of pain from cancer and its treatments, the ability to evaluate cancer pain continues to be difficult due to the complexity of the disease and the subjective experience of pain. The Oncology Section Breast Cancer EDGE Task Force was created to evaluate the evidence behind clinical outcome measures in women diagnosed with breast cancer. Methods: The authors systematically reviewed the literature for pain outcome measures published in the research involving women diagnosed with breast cancer. The goal was to examine the reported psychometric properties that are reported in the literature in order to determine clinical utility. Results: Visual Analog Scale, Numeric Rating Scale, Pressure Pain Threshold, McGill Pain Questionnaire, McGill Pain Questionnaire - Short Form, Brief Pain Inventory and Brief Pain Inventory - Short Form were highly recommended by the Task Force. The Task Force was unable to recommend two measures for use in the breast cancer population at the present time. Conclusions: A variety of outcome measures were used to measure pain in women diagnosed with breast cancer. When assessing pain in women with breast cancer, researchers and clinicians need to determine whether a unidimensional or multidimensional tool is most appropriate as well as whether the tool has strong psychometric properties.
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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 | 0.010 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".