Bibliographic record
Abstract
OBJECTIVES: To identify empirical subtypes of schizophrenia, based upon the symptoms recorded over the duration of the illness, and to validate the resulting clusters against other systems that are used for subtyping schizophrenia. METHOD: Data for 55 symptoms of schizophrenia over the history of the illness from 107 chronic schizophrenia patients were analyzed using hierarchical cluster analysis with Euclidean distance and Ward's method. Except for 1 patient, all met DSM-III criteria. There were 40 men and 67 women, average (SD) age of 38.2 (9.91) years, with a mean (SD) hospitalization of 27.9 (27.35) months. RESULTS: No clear and unambiguous solution for the number of clusters was evident. Examination of the clusters led to further analysis of 2- and 6-cluster solutions. These were contrasted with DSM-III, DSM-III-R, and DSM-IV criteria and with the subtypes taken from the literature. There was limited support for any of these types, with none replicating, including the paranoid-nonparanoid distinction. CONCLUSIONS: Empirical clusters derived from lifetime symptom data failed to agree with either the established DSM or other empirically derived subtypes. Subtypes may have little utility when the variability of symptoms over the longitudinal course of the illness is considered.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".