Influence of Prior Cognitive Impairment on the Severity of Delirium Symptoms Among Older Patients
Bibliographic record
Abstract
Delirium is common among hospitalized elderly patients with prior cognitive impairment. Detecting delirium superimposed on dementia is a challenge for nurses and doctors. As a result, delirium among demented elderly patients is of increasing interest to healthcare professionals. So far, studies have failed to describe how symptoms of delirium are altered by severity of dementia. This would be valuable information to improve the rate of detection by nurses of delirium among demented patients. However, until now no research has examined the effect of severity of prior cognitive impairment on the severity of delirium symptoms among institutionalized older patients. This study describes the effect of severity of prior cognitive impairment on the severity of delirium symptoms among institutionalized older patients with delirium at the time of their admission to an acute care hospital. One hundred four institutionalized elderly people were included in this study and screened for delirium using the confusion assessment method. Patients with delirium (n = 71) were evaluated with the delirium index to determine the severity of the symptoms of delirium. The results showed that the severity of prior cognitive impairment influences the severity of most of the symptoms of delirium, particularly disordered attention, orientation, thought organization, and memory. Thus, taking into account the severity of prior cognitive impairment could help nurses to detect delirium among older patients.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| 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".