Psychotic Symptoms as Manifestations of the Posttraumatic Confusional State
Bibliographic record
Abstract
OBJECTIVES: To (1) determine factors associated with psychotic-type symptoms in persons with moderate or severe traumatic brain injury (TBI) during early recovery and (2) investigate the prognostic significance of early psychotic-type symptoms for patient outcome. SETTING: Acute neurorehabilitation inpatient unit. PARTICIPANTS: A total of 168 persons with moderate or severe TBI were admitted for inpatient rehabilitation. Of these, 107 had psychotic-type symptoms on at least 1 examination. One-year productivity outcome was available for 87 of the 107 participants. DESIGN: Prospective, inception cohort, observational study. MAIN MEASURES: Confusion Assessment Protocol, productivity outcome at 1 year postinjury. RESULTS: Presence of sleep disturbance, a shorter interval from admission to assessment, and greater cognitive impairment were associated with a greater incidence of psychotic-type symptoms. Younger age, more years of education, and lower frequency and severity of psychotic-type symptoms were associated with a greater likelihood of favorable productivity outcome. CONCLUSIONS: We identified risk factors for the occurrence of psychotic-type symptoms and extended previous findings regarding the significance of these symptoms for outcome after TBI. These findings suggest that improved sleep in early TBI recovery may decrease the occurrence of psychotic-type symptoms.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".