The 5-Year Incidence of Mental Disorders in a Population-Based ICU Survivor Cohort
Bibliographic record
Abstract
OBJECTIVE: To estimate incidence of newly diagnosed mental disorders among ICU patients. DESIGN: Retrospective-matched cohort study using a population-based administrative database. SETTING: Manitoba, Canada. PARTICIPANTS: A total of 49,439 ICU patients admitted between 2000 and 2012 were compared with two control groups (hospitalized: n = 146,968 and general population: n = 141,937), matched on age (± 2 yr), sex, region of residence, and hospitalization year. INTERVENTION: None. MEASUREMENTS AND MAIN RESULTS: Incident mental disorders (mood, anxiety, substance use, personality, posttraumatic stress disorder, schizophrenia, and psychotic disorders) not diagnosed during the 5-year period before the index ICU or hospital admission date (including matched general population group), but diagnosed during the subsequent 5-year period. Multivariable survival models adjusted for sociodemographic variables, Charlson comorbidity index, admission diagnostic category, and number of ICU and non-ICU exposures. ICU cohort had a 14.5% (95% CI, 14.0-15.0) and 42.7% (95% CI, 42.0-43.5) age- and sex-standardized incidence of any diagnosed mental disorder at 1 and 5 years post-ICU exposure, respectively. In multivariable analysis, ICU cohort had increased risk of any diagnosed mental disorder at all time points versus the hospitalized cohort (year 5: adjusted hazard ratio, 2.00; 95% CI, 1.80-2.23) and the general population cohort (year 5: adjusted hazard ratio, 3.52; 95% CI, 3.23-3.83). A newly diagnosed mental disorder was associated with younger age, female sex, more recent admitting years, presence of preexisting comorbidities, and repeat ICU admission. CONCLUSIONS: ICU admission is associated with an increased incidence of mood, anxiety, substance use, and personality disorders over a 5-year period.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".