Childhood catatonia, autism and psychosis past and present: is there an ‘iron triangle’?
Bibliographic record
Abstract
OBJECTIVE: To explore the possibility that autism, catatonia and psychoses in children are different manifestations of a single underlying form of brain pathology - a kind of 'Iron Triangle' of symptomatology - rather than three separate illnesses. METHOD: Systematic evaluation of historical case literature on autism to determine if catatonic and psychotic symptoms accompanied the diagnosis, as is found in some challenging present-day cases. RESULTS: It is clear from the historical literature that by the 1920s all three diagnoses in the Iron Triangle - catatonia, autism and childhood schizophrenia - were being routinely applied to children and adolescents. Furthermore, it is apparent that children diagnosed with one of these conditions often qualified for the other two as well. Although conventional thinking today regards these diagnoses as separate entities, the presence of catatonia in a variety of conditions is being increasingly recognized, and there is also growing evidence of connections between childhood-onset psychoses and autism. CONCLUSION: Recognition of a mixed form of catatonia, autism and psychosis has important implications for both diagnosis and treatment. None of the separate diagnoses provides an accurate picture in these complex cases, and when given single diagnoses such as 'schizophrenia', the standard treatment options may prove markedly ineffective.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".