Automated pain intervention for underserved minority women with breast cancer
Bibliographic record
Abstract
BACKGROUND: Minority patients with breast cancer are at risk for undertreatment of cancer-related pain. The authors evaluated the feasibility and efficacy of an automated pain intervention for improving pain and symptom management of underserved African American and Latina women with breast cancer. METHODS: Sixty low-income African American and Latina women with breast cancer and cancer-related pain were enrolled in a pilot study of an automated, telephone-based, interactive voice response (IVR) intervention. Women in the intervention group were called twice weekly by the IVR system and asked to rate the intensity of their pain and other symptoms. The patients' oncologists received e-mail alerts if the reported symptoms were moderate to severe. The patients also reported barriers to pain management and received education regarding any reported obstacles. RESULTS: The proportion of women in both groups reporting moderate to severe pain decreased during the study, but the decrease was significantly greater for the intervention group. The IVR intervention also was associated with improvements in other cancer-related symptoms, including sleep disturbance and drowsiness. Although patient adherence to the IVR call schedule was good, the oncologists who were treating the patients rated the intervention as only somewhat useful for improving symptom management. CONCLUSIONS: The IVR intervention reduced pain and symptom severity for underserved minority women with breast cancer. Additional research on technological approaches to symptom management is needed.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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) | 0.004 | 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".