Utilisation des psychostimulants chez les patients en fin de vie atteints de delirium hypoactif et de troubles cognitifs: Revue de la littérature
Bibliographic record
Abstract
OBJECTIVE: To review the research about psychostimulant effects on cognitive functions in end-of-life patients diagnosed with hypoactive delirium or cognitive disorders. METHOD: The MEDLINE (1966-March 2008), Embase (1974-March 2008), PsycINFO (1806-March 2008), IPA (1970-March 2008), CINAHL (1982-March 2008), ISI Web of Science (1945-March 2008), Current Contents (March 2007-March 2008), Access Medicine (2001-March 2008), and ProQuest Dissertations & Theses (1980-March 2008) databases were searched with keywords related to delirium, cognition, psychostimulants, and palliative care for French or English articles in a dementia-free and hyperactive delirium-free end-of-life population. Cognitive functions had to be assessed before and after initiation of the psychostimulant treatment. Moreover, treatment had to be initiated after the onset of cognitive impairments. RESULTS: A total of 173 studies were screened. Five studies on methylphenidate and 1 study on caffeine met inclusion criteria and were included in this review. Two studies were case reports, 2 were open-label trials, and 2 were double-blind, crossover randomized placebo-controlled trials. Three studies were conducted with hypoactive delirium patients and all studies were conducted in an advanced cancer patient population. CONCLUSIONS: The reviewed studies support the use of methylphenidate to improve end-of-life patient cognitive functions, particularly in the case of hypoactive delirium. Caffeine seems to have beneficial effects on psychomotor activity. Further well-designed studies are needed to consolidate these findings.
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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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".