Hidden in Plain Sight: Recognizing Catatonia Amidst its Medical Complications
Bibliographic record
Abstract
ABSTRACT:Although catatonia is a common syndrome, diagnosis is often delayed or missed altogether. The medical sequelae of catatonia can cloud the diagnostic picture, making it difficult to know what is the primary problem. In this case, a patient presented several times about 1 month apart with recurrent urinary retention, inability to walk, and delirium. This resulted in admissions to Internal Medicine and consultations to Urology with the underlying primary problem being missed until catatonia was later recognized and diagnosed. The elderly are more prone to complications from catatonia and, as a result, it is even more important that catatonia be recognized and treated in a timely manner in this population. In addition to exploring the case, this article reviews the diagnosis, etiology, prognosis, and treatment of catatonia, particularly as these pertain to the elderly.RÉSUMÉ:La catatonie est un syndrome commun, mais son diagnostic est parfois difficile à faire. Les symptômes associés à ce syndrome peuvent rendre le problème médical primaire difficile à déceler. Par exemple, l’association récurrente de symptômes de rétention urinaire, des difficultés à marcher et des signes de syndrome confusionnel qui se présentent chaque mois sont souvent associés à d’autres maladies. Ces manifestations symptomatiques mènent à des admissions en médecine interne et des consultations en urologie. Les cliniciens peuvent perdre de vue le problème primaire, celui de la catatonie. Le diagnostic est alors manqué ou découvert plus tard. Les personnes âgées sont plus susceptibles à des complications liées à la catatonie. Ainsi, il s’avère important que ce désordre soit reconnu et traité dans un délai raisonnable chez cette population. Cet article évalue le diagnostic, l’étiologie, le pronostic et le traitement de la catatonie, particulièrement dans le cadre des personnes âgées.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".