Subtyping Schizophrenia According to Outcome or Severity: A Search for Homogeneous Subgroups
Bibliographic record
Abstract
There is a growing consensus that current definitions of schizophrenia (SZ) include different disorders, or else different dimensions underlain by different pathophysiologies. This article reviews the evidence for the validity of three novel strategies to subtype SZ according to outcome or severity (deficit vs. nondeficit, Kraepelinian vs. non-Kraepelinian, congenital vs. adult-onset). Medline and bibliographies were used to locate articles. The methodology of the studies was reviewed, and their results were grouped according to seven validating criteria. Several differences were found between subtypes, particularly for the deficit/nondeficit subtypes. However, for most of these differences, replications have yet to be undertaken. Important indicators of etiology from the environmental risk factors and genetic domains have received very little attention. These three subtyping strategies represent promising attempts to address the etiologic heterogeneity of SZ. However, one cannot conclude whether these strategies identify etiologically distinct SZ subgroups. We propose ten methodological and conceptual recommendations for future studies aimed at the identification of valid SZ subtypes according to outcome or severity.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".