Factors That Determine Satisfaction With Surgical Treatment of Low-Income Women With Breast Cancer
Bibliographic record
Abstract
OBJECTIVE: To analyze the relationship between patient satisfaction with surgical treatment and 4 consultation skills and processes of the surgeons (time spent, listens carefully, explains concepts in a way the patient can understand, and shows respect for what the patient has to say), controlling for a range of patient, surgeon, and treatment characteristics. DESIGN: Cross-sectional survey. SETTING: The Breast and Cervical Cancer Treatment Program for the state of California. PATIENTS: A statewide sample of 789 low-income women who received treatment for breast cancer from February 1, 2003, through September 31, 2005. MAIN OUTCOME MEASURE: Satisfaction with surgical treatment. RESULTS: Three of every 4 women reported being extremely satisfied with the treatment they received from their surgeon. African American women and those with arm swelling were less likely to be satisfied, whereas those reporting that the surgeon always spent enough time and explained concepts in a way they could understand were more likely to report greater satisfaction. CONCLUSION: Our findings highlight the importance of 2 relatively simple behaviors that surgeons can easily implement to increase patient satisfaction, which can be of potential benefit in the litigious world of today.
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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 | 0.001 | 0.005 |
| 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".