Evaluation of Association of Impairment of Attention with Other Symptoms of Delirium
Bibliographic record
Abstract
Aim: To evaluate the association of impairment of attention with other symptoms of delirium. Methodology: Eighty-six patients with delirium as per the Diagnostic and Statistical Manual, 5 th Revision seen in the consultation-liaison psychiatry setup of a tertiary care hospital were cross-sectionally assessed on the short informant questionnaire on cognitive decline in the elderly (Retrospective), montreal cognitive assessment (MoCA), and delirium rating scale revised-98 (DRS-R98) version. Results: The mean age of the study participants was 46.6 (standard deviation [SD] – 16.4) years. All the patients had impairment in attention with the altered sleepwake cycle, acute onset of illness, with the fluctuating course and underlying physical disease. In terms of severity, the severity score was the highest for the item of sleep-wake cycle disturbances, followed by motor agitation. The mean noncognitive symptoms domain of the DRS-R98 domain were more than the mean score of the cognitive symptom domain of DRS-R98. The mean total score on MoCA was 11.9 (SD: 7.5). Higher attention impairment was associated with more severe noncognitive and cognitive symptoms and higher delirium severity as assessed by DRS-R98. Higher severity of attention deficit was also associated with higher impairment in other domains of cognition of MoCA. Cognitive symptoms, as evaluated by DRS-R98, had more significant correlations with various domains of MoCA except for language and abstraction. Conclusion: Attention deficits are the core symptom of delirium and have a significant impact on other cognitive and noncognitive symptoms of delirium.
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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 | 0.004 |
| 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 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".