Bruno Schulz's 1930 article “The Hereditary Relationships of Old‐Age Paranoid Psychosis”
Bibliographic record
Abstract
In the 1899 6th edition of his influential textbook, Kraepelin proposed a diagnostic category of "Old-Age Paranoid Psychosis." In this 1930 article, Bruno Schulz studied the morbid risk (MR) of several disorders and traits in the parents, siblings, offspring, and nieces/nephews of 51 probands with "Old-Age Paranoid Psychosis." His results permitted an evaluation of the validity of Kraepelin's category of Old-Age Paranoid Psychosis, in particular, whether it was a form of psychosis resulting from "senile changes" or late-onset schizophrenia. The MR of schizophrenia in these four groups of relatives varied from 0 to 2.4% with 3 of 4 somewhat higher than population expectations but much lower than parallel results in relatives of schizophrenics. By contrast, the rates of eccentricity in these relatives were uniformly elevated over population rates, sometimes approaching those seen in relatives of schizophrenics. Schulz concluded, from his study, that Old-Age Paranoid Psychosis was a distinct disorder not closely related to schizophrenia. However, he suggested that a family history and/or a premorbid trait of eccentricity increases the risk of developing a paranoid psychosis in old age, particularly when associated with physical or mental decline. He was uncertain about whether the trait of eccentricity he found in this study was very similar or distinct from that observed in excess in relatives of schizophrenics. This study was the first, to the best of our knowledge, to use a family study design explicitly to address a nosologic question-in this case the familial relationship between Old-Age Paranoid Psychosis and schizophrenia.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".