Bibliographic record
Abstract
OBJECTIVE: To outline current approaches to diagnosing and managing delirium in the elderly. QUALITY OF EVIDENCE: A literature review was based on a MEDLINE search (1966 to 1998). Selected articles were reviewed and used as the basis for discussion of diagnosis and etiology. We planned to include all published randomized controlled trials regarding management but found only two. Consequently, we also used review articles and recent practice guidelines for delirium published by the American Psychiatric Association. MAIN FINDINGS: Clinical diagnosis of delirium can be aided by using DSM-IV criteria, the Delirium Symptom Interview, or the confusion assessment method. Management must include investigation and treatment of underlying causes and general supportive measures. Providing optimal levels of stimulation, reorienting patients, education, and supporting families are important. Pharmacologic management of delirium should be considered only for specific symptoms or behaviours, e.g., aggression, severe agitation, or psychosis. Only one randomized controlled trial of tranquilizer use for delirium in medically ill people has been published. Findings support the current belief that neuroleptics are superior to benzodiazepines in most cases of delirium. Most authorities still consider haloperidol the neuroleptic of choice. Controlled trials of the new atypical neuroleptics for treating delirium are not yet available. Benzodiazepines with relatively short half-lives, such as lorazepam, are the drugs of choice for withdrawal symptoms. CONCLUSION: Delirium is frequently underdiagnosed in clinical practice. It should be suspected with acute changes in behaviour. Careful investigation of the underlying cause permits appropriate management.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 teacher head, 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".