How Successful Are Physicians in Eliciting the Truth From Their Patients?
Bibliographic record
Abstract
OBJECTIVE: How honestly patients report their symptoms and medication adherence to their physicians has not been adequately addressed in patients with depression. We therefore conducted a large-scale Internet survey in an effort to discover how successful physicians are in eliciting the truth from their patients and also to examine reasons for patients' truth-concealing behaviors. METHOD: 2,354 participants who had received treatment for depression within the past year and had been diagnosed with depression by Patient Health Questionaire were identified from 323,226 registrants at the Macromill database through screening procedures. Participants were asked to complete a questionnaire regarding their treatment for depression with a special focus on patient-physician relationship. This study was conducted from December 7 to 13, 2010, in Japan. RESULTS: 2,020 participants successfully completed the questionnaire. Overall, 70.2% of responders reported that they had withheld the truth from their physicians. A logistic regression model found significant associations of such a behavior with female sex (95% CI, 1.15-1.74; P = .001), younger age (95% CI, 0.49-0.97; P = .030), and a lower degree of satisfaction in mutual communication (95% CI, 3.17-6.58; P < .001). 69.2% and 52.6% of the participants refrained from telling about their "daily activities" and "symptoms," respectively. Female participants were more likely to hide the facts concerning "adherence to prescribed medication" and "figures such as body temperature and weight." 31.9% of participants had discontinued the treatment without consulting their physician, which was again more frequent in females, younger persons, and those who were not satisfied with communication with their physician. CONCLUSIONS: While the findings obtained herein need to be replicated in other patient populations, a majority of patients with depression were reluctant to uncover the truth, which emphasizes the need for more fine-tuned suspicion among physicians about symptoms and medication adherence.
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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 | 0.016 | 0.105 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".