Bibliographic record
Abstract
Mortality rates have consistently been shown to be greater in patients with delirium compared to those without. Published work over the last decade has revealed however, that several confounding factors play key roles in contributing to the excess mortality in the delirium population and that statistical adjustment for these factors in multivariate analyses minimizes, if not eliminates, the association between delirium and mortality. These factors include pre-existing dementia, advanced age, severe medical illness, diminished functional status, and intoxication or withdrawal from medications. However, studies on prognosis and prognostic indicators of delirium in the past have been limited to subjects admitted to the hospital where the sample may include both incident and prevalent cases of delirium. Objective. To determine whether prevalent delirium is an independent predictor for mortality among elderly patients seen in the Emergency department. Potentially confounding factors were assessed to reveal their prognostic contributions in this population. Survival analysis was carried out using the Cox Proportional Hazards Modelling technique. Methods. As part of a larger study, 268 patients seen in the Emergency department in two Montreal hospitals (107 delirium cases, 161 controls) were followed up in 6 month intervals for a total of 18 months. Dates of deaths for the deceased were obtained from the Ministere de la Sante et des Service Sociaux. Results. The analysis revealed a non-significant association between delirium and mortality rate for the English speaking subjects, when adjusted for age, sex, pre-morbid cognitive decline (IQCODE), Basic ADL, Instrumental ADL, comorbidity, number of medication, education (years), eyesight, and hearing problems (p = 0.752, HR =1 .095, CI: 0.622--1.929). On the other hand, for the French speaking subjects, the same model revealed a highly significant association between delirium and death rate (p = 0.001, HR = 9.078, CI: 2.362--34.892). Possible explanations for the different results are discussed.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".