Bibliographic record
Abstract
Editor's note Delirium is one of the forgotten areas of therapeutics. It is observed frequently, often misinterpreted and misunderstood, but often mercifully disappears just as uncertainty about what to do gets stronger. This probably explains the relative paucity of evidence available about preferred treatments; delirium is a prelude to focused intervention rather than a clarion call for action. This chapter nonetheless indicates the beginnings of an evidence base for intervention that is of definite value. Introduction Delirium is associated with increased rates of mortality and medical complications, prolonged hospitalization, as well as cognitive and functional impairment. Symptoms of delirium can also cause significant distress and discomfort to both patients and their families. Unfortunately, delirium is commonly underdiagnosed by physicians. Recognition and appropriate treatment of delirium is essential to minimize associated morbidity. The treatment of delirium is to tackle the underlying cause. Identifying underlying aetiologies and their correction should always be foremost in the clinician's mind. The common causes include infection, drug intoxication, renal or hepatic insufficiency, vascular disease affecting the brain, and electrolyte disturbance, and the prevalence of the condition varies from 10% in young hospitalized medical patients to 80% in those who are terminally ill (Brown & Boyle, 2002). Delirium may cause agitation or psychotic symptoms, which puts patients or others at risk, affects their treatment, or may cause significant distress. In addition to environmental interventions, pharmacological treatment may be necessary to control these symptoms. Antipsychotics have been the mainstay of treatment for agitation and psychotic symptoms in delirium.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.081 | 0.048 |
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".