Room transfers and the risk of delirium incidence amongst hospitalized elderly medical patients: a case–control study
Bibliographic record
Abstract
BACKGROUND: Room transfers are suspected to promote the development of delirium in hospitalized elderly patients, but no studies have systematically examined the relationship between room transfers and delirium incidence. We used a case-control study to determine if the number of room transfers per patient days is associated with an increased incidence of delirium amongst hospitalized elderly medical patients, controlling for baseline risk factors. METHODS: We included patients 70 years of age or older who were admitted to the internal medicine or geriatric medicine services at St. Michael's Hospital between October 2009 and September 2010 for more than 24 h. The cases consisted of patients who developed delirium during the first week of hospital stay. The controls consisted of patients who did not develop delirium during the first week of hospital stay. Patients with evidence of delirium at admission were excluded from the analysis. A multivariable logistic regression model was used to determine the relationship between room transfers and delirium development within the first week of hospital stay. RESULTS: 994 patients were included in the study, of which 126 developed delirium during the first week of hospital stay. Using a multivariable logistic regression model which controlled for age, gender, cognitive impairment, vision impairment, dehydration, and severe illness, room transfers per patient days were associated with delirium incidence (OR: 9.69, 95 % CI (6.20 to15.16), P < 0.0001). CONCLUSIONS: An increased number of room transfers per patient days is associated with an increased incidence of delirium amongst hospitalized elderly medical patients. This is an exploratory analysis and needs confirmation with larger studies.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| 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".