Descriptive Study of Patients Treated in a Psychosomatic Internal Medicine Declared by Japanese Family Medicine Clinic
Bibliographic record
Abstract
Background: Psychosomatic internal medicine (PSIM) assesses psychosocial factors and provides holistic consideration. In Japan, PSIM physicians seem to be recognized as providers of mental health services, but family medicine did not so. When family physicians confront with psychological problems, high dropout rate is reported so it is needed to reveal factors related to dropouts, The purpose of this study is to describe characteristics of patients, treatment dropouts and its related factors in PSIM practice by family physician. Methods: This cross-sectional study used data from the medical records of the Kitaibaraki Center of Family Medicine located in Kitaibaraki City, Ibaraki, Japan. The study included all new patients who made an appointment and visited the PSIM in this clinic from January 2020 to December 2022.Chief complaints and diagnoses were coded based on the International Classification of Primary Care, version 2 (ICPC-2). Results: In total, 377 new patients were included in this study. The mean age was 39.9 ± 20.2 years. We found that 69.2% of patients who visited the clinic had a psychological chief complaint and 84.1% of primary diagnoses consisted of a psychological problem. One hundred sixty-five patients (43.8%) were still receiving treatment 6 months after the initial visit. Of the patients who ended treatment within 6 months after the initial visit, 84 patients (39.2%) dropped out. In multivariate analysis, the dropouts were less likely to occur patients with primary diagnosis of psychological problem (odds ratio (OR): 0.35, 95% confidence interval (CI): 0.19 - 0.67). Conclusions: Patients who visited a PSIM wanted consultation about psychological problems. Patients with a diagnosis of a psychological problem at the initial visit were less likely to drop out.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".