Distinguishing Bipolar Depression, Major Depression, and Schizophrenia With the MMPI-2 Clinical and Content Scales
Bibliographic record
Abstract
Clinical and content scales from the MMPI-2 (Butcher, Dahlstrom, Graham, Tellegen, & Kaemmer, 1989) were used to examine the capacity of these scales to assist in the differential diagnosis of a sample of 212 psychiatric patients-137 with major depression; 43 with schizophrenia; and 32 with bipolar disorder, depressed state. Consistent with the previous literature, the clinical scales Depression (D), and Schizophrenia (Sc), and the content scales Depression (DEP), and Low Self-Esteem (LSE) best distinguished major depression from schizophrenia; the content scale DEP proved to be the most powerful predictor in distinguishing bipolar depression from schizophrenia. No clinical or content scale proved to be effective in distinguishing patients with bipolar depression from patients with major depression. In general, the content scales outperformed the clinical scales.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".