Diagnostic Concepts and the Prevention of Schizophrenia
Bibliographic record
Abstract
Schizophrenia has long been recognized as a devastating disorder for patients and their families. Although substantial progress has been achieved in both its diagnosis and treatment, and in understanding the disorder’s neurobiological substrates, a full understanding of its origins and pathogenic mechanisms remains elusive. One obstacle to better understanding the causes of schizophrenia may be its diagnostic criteria (1). The DSM-IV and other nosologies provide a foundation for clinical diagnosis, but there is little basis for regarding the DSM’s operational definition as the “true” construct of schizophrenia. In the 1970s and 1980s, narrow diagnostic criteria for disorders like schizophrenia were needed to improve the reliability of clinical diagnoses. Research has also benefited from the reliability of recent DSMs, in that clinical characteristics of samples are more standardized across studies and thus more easily replicated. Moreover, the use of stringent diagnostic criteria laid the groundwork for studies to assess and demonstrate the validity of schizophrenia. For example, schizophrenia can be delineated from other disorders, it shows familial loading, and it demonstrates predictable measures of outcome. Yet, despite the many benefits of classification systems such as the DSM, could the classification of schizophrenia be improved by integrating current knowledge with existing conceptual and classificatory schemes? In other words, can the reliability of the DSM-IV diagnosis of schizophrenia be retained while its validity is increased? In this context, at least 3 limitations of the current DSM diagnosis can be addressed: its view that schizophrenia is a discrete category, its use of descriptive criteria that ignore information about the etiology
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 | 0.009 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".