Environmental Risk Factors for Delirium in Hospitalized Older People
Bibliographic record
Abstract
OBJECTIVES: To evaluate the relationship of environmental risk factors in hospitals to changes over time in delirium symptom severity scores. DESIGN: Observational prospective clinical study with repeated measurements, several times during the first week of hospitalization and then weekly during hospitalization. SETTING: University-affiliated general community hospital. PARTICIPANTS: Four hundred forty-four patients age 65 and older admitted to the medical wards: 326 with delirium and 118 without delirium. Patients with prior cognitive impairment were oversampled. MEASUREMENTS: The severity of delirium symptoms was measured with the Delirium Index, a scale developed and validated by our group, based on the Confusion Assessment Method. Potential environmental risk factors assessed included isolation, hospital unit, room changes, levels of sensory stimulation, aids to orientation, and presence of medical (e.g., intravenous) or physical restraints. RESULTS: Controlling for initial severity of delirium and patient characteristics, variables significantly related to an increase in delirium severity scores included hospital unit (intensive care or long-term care unit), number of room changes, absence of a clock or watch, absence of reading glasses, presence of a family member, and presence of medical or physical restraints. CONCLUSION: The associations of intensive care and medical and physical restraints with severity of delirium symptoms may be due to uncontrolled confounding by indication. However, the other factors identified suggest potentially modifiable risk factors for symptoms of delirium in hospitalized older people.
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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 | 0.001 |
| 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".