Landmark's Model for Computerized Studies of Schizophrenia: A Review
Bibliographic record
Abstract
Johan A. Landmark, a Canadian scientist and clinical psychiatrist of Norwegian origin, directed a large scale investigation on a sample of 120 Canadian schizophrenic patients that were rated on 87 symptoms relevant for the assessment of schizophrenia and on a multitude of sociodemographic and case history variables in order to determine if specific statistical symptom patterns would emerge as computerized clinical predictors of response to psychiatric medication, or how the symptom patterns would relate to sociodemographic variables such as gender, education, birth-order, and age.The specific symptoms and other patients' variables that significantly correlated with outcomes of psychiatric medication were entered in a statistical regression equation as mathematical predictors for future pharmacological treatments.With respect to outcomes of fluphenazine treatment at that time, the best predictor was a triad of symptoms including auditory hallucinations, passivity feelings, and disturbances of affect.This statistical approach needs to be replicated for novel antipsychotics and substances such as cannabidiol to generate statistically based predictions of which medication is the best for the individual patient with his or her particular symptom pattern, to avoid the prevalent lengthy and frustrating "trial and error" routines in daily clinical psychiatry.The various symptoms of schizophrenia in Landmark's sample were not strongly related to any sociodemographic variables: this supports biopsychiatric concepts of schizophrenia as opposed to those based on psychosocial factors.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| 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".