EPA-0291 – What clinical and social factors determine whether patients with schizophrenia work or not?
Bibliographic record
Abstract
The purpose of this study is finding out clinical and social factors which determine whether patients with schizophrenia work or not. Subjects are two-hundred sixty outpatients with schizophrenia or schizoaffective disorder from 3 hospital in this study. Clinical symptom and social function were measured with PANSS(Positive And Negative Syndrome Scale), GAPS(Global Assessment of Psychopathology Scale), SOFAS(Social and Occupational Functioning Assessment Scale), SDS(Self-rating Depression Scale), CDRS(Connor-Davidson Resilience Scale), SAI-J(Schedule for Assessment of Insight-Japanese edition), DAI-10(Drug Attitude Inventory-10), JCDSS(Japanese Calgary Depression Scale for Schizophrenics), SFS(Social Functioning Scale), JSQLS(Japanese Schizophrenia Quality of Life Scale), DUP(Duration of Untreated Period) and we researched the social situation of the patients about life history, familial history, type and dose of antipsychotics, age of onset, history of hospitalization, years of schooling, whether to use social welfare services or not. At this time, this study is still in progress. The research of only patients with schizophrenia who is working has finished. Although it is deferent from main purpose, we classified the patients who is working into two groups(remission/non-remission) based on a definition of the remission of Andreasen and compared two groups(Table1,2,3). We’ll be able to report on the results including patients with schizophrenia who is not working. And we expect finding out the factors which determine whether they work or not.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".