Catatonia and elevated mortality: A population‐wide cohort study with healthy, sibling, and schizophrenia spectrum controls
Bibliographic record
Abstract
AIM: To determine whether catatonia is associated with increased long-term all-cause and cause-specific mortality. METHODS: Using Taiwan's National Health Insurance Database (2000-2022), we assembled a population-based cohort of all adults (≥18 years) with catatonia and matched each to four controls without catatonia on sex and birthdate. Mortality was compared between (1) individuals with catatonia and their unaffected siblings and (2) individuals with schizophrenia spectrum disorders with catatonia and those with schizophrenia spectrum disorders without catatonia. The primary outcome was all-cause mortality; secondary outcomes were natural- and unnatural-cause deaths. Adjusted hazard ratios (HRs) with 95% confidence intervals (CIs) were estimated with Cox models controlling for age, sex, socioeconomic status, urbanization level, and comorbidities. RESULTS: We included 6642 individuals with catatonia and 26,568 matched controls. Over mean follow-ups of 11.4 and 13.1 years, respectively, 2150 versus 3459 deaths occurred (adjusted HR 2.60, 95% CI 2.46-2.75). Risks were higher for natural causes (2.42, 2.28-2.57) and unnatural causes (5.57, 4.59-6.77). Compared with unaffected siblings, catatonia remained associated with excess all-cause (1.82, 1.34-2.49), natural (1.57, 1.07-2.30), and unnatural mortality (2.73, 1.56-4.77). Within schizophrenia spectrum disorders, catatonia conferred higher all-cause (1.20, 1.12-1.28) and natural mortality (1.27, 1.18-1.36), whereas unnatural mortality was similar (1.01, 0.87-1.17). CONCLUSIONS: Catatonia conferred a substantial, independent risk of premature mortality across multiple causes. Clinicians should recognize that catatonia is a serious disorder with long-term consequences and should remain vigilant to prevent and manage complications beyond the acute episode.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".