Humor in radiological breast cancer screening: a way of improving patient service?
Bibliographic record
Abstract
BACKGROUND: Breast cancer screening is essential in detecting breast tumors, however, the examination is stressful. In this study we analyzed whether humor enhances patient satisfaction. METHODS: In this prospective randomized study 226 patients undergoing routine breast cancer screening at a single center during October 2020 to July 2021 were included. One hundred thirty-two were eligible for the study. Group 1 (66 patients) received an examination with humorous intervention, group 2 (66 patients) had a standard breast examination. In the humor group, the regular business card was replaced by a self-painted, humorous business card, which was handed to the patient at the beginning of the examination. Afterwards, patients were interviewed with a standardized questionnaire. Scores between the two study groups were compared with the Mann-Whitney U test or Fisher's exact test. P-values were adjusted with the Holm's method. Two-sided p-values < 0.05 were considered significant. RESULTS: One hundred thirty-two patients, 131 female and 1 male, (mean age 59 ± 10.6 years) remained in the final study cohort. Patients in the humor group remembered the radiologist's name better (85%/30%, P < .001), appreciated the final discussion with the radiologist more (4.67 ± 0.73-5;[5, 5] vs. 4.24 ± 1.1-5;[4, 5], P = .017), felt the radiologist was more empathetic (4.94 ± 0.24-5;[5, 5] vs.4.59 ± 0.64-5;[4, 5], P < .001), and rated him as a humorous doctor (4.91 ± 0.29-5;[5, 5] vs. 2.26 ± 1.43-1;[1, 4], P < .001). Additionally, patients in the humor group tended to experience less anxiety (p = 0.166) and felt the doctor was more competent (p = 0.094). CONCLUSION: Humor during routine breast examinations may improve patient-radiologist relationship because the radiologist is considered more empathetic and competent, patients recall the radiologist's name more easily, and value the final discussion more. TRIAL REGISTRATION: We have a general approval from our ethics committee because it is a retrospective survey, the patient lists for the doctors were anonymized and it is a qualitative study, since the clinical processes are part of the daily routine examinations and are used independently of the study. The patients have given their consent to this study and survey.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".