Bibliographic record
Abstract
PURPOSE OF REVIEW: The past few years have witnessed increased research into delirium treatment and related issues, leading to better management (e.g. improved detection) and better understanding of phenomenology and pathophysiology. Many treatment and prevention trials have been conducted. RECENT FINDINGS: Delirium phenomenology studies revealed that even subsyndromal presentations may bear a poor prognosis. Varied pathophysiology may lead to different delirium subtypes with implications for treatment, especially the hypoactive subtype, for which systematic neuroleptic treatment remains controversial. The high prevalence of delirium has led to improved use of validated instruments and better trials. Nonpharmacological interventions remain an essential step in delirium management and have yielded positive results, especially in prevention. Two trials of haloperidol prophylaxis identified reduced severity and duration of delirium in one and reduced incidence in the other. Trials comparing haloperidol with atypical antipsychotics, mainly risperidone and olanzapine, found equal efficacy but more side effects with haloperidol. SUMMARY: Use of validated detection instruments is now standard procedure in both specialized clinical practice and research. Although haloperidol remains the mainstay of treatment, recent trials have begun to discriminate between the use of different agents and pharmacological approaches.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".