Antipsychotics in dementia - mortality risks and strategies to reduce prescribing
Bibliographic record
Abstract
For the UK, the first governmental warning about prescribing antipsychotics in dementia came as a safety briefing in 2004 alerting clinicians to the risks of stroke with two of the atypical antipsychotics.1 Meanwhile, meta-analytic evidence was accumulating across the pond pointing not only to increased risk of stroke but also sedation, urine infections, parkinsonism and cognitive deterioration.2 It was these concerns that led the Department of Health to commission a report from Professor Sube Banerjee on the use of antipsychotics in dementia.3 Published as ‘Time for Action’ in October 2009, Banerjee's detailed review of the RCT literature concluded that, for treatments between 6 and 12 weeks, there was minimal evidence for improvement in global behavioural disturbance (effect size range 0.1–0.2),2 but an increased absolute mortality risk of 1% (NNH 100 (95% CI 50 to 250)).4 Banerjee applied these statistics to the available figures for UK prevalence of antipsychotic prescribing in people with dementia, and calculated the staggering headline figure of 1800 deaths being directly related to these drugs every year.3 However, as acknowledged (if not emphasised) by Banerjee, this alarming figure appears to be a highly conservative underestimate. First, the literature suggests that the prescribing of antipsychotics in dementia continues way beyond the 10–12 weeks used in Banerjee's equation. For example, one small American study (n=58) found a mean prescription length of 16.5 months (median 9 months).5 These findings were replicated in a larger Canadian study in care homes (n=1017) where mean prescription length was 1.02 years (median 34 weeks).6 If the risk of death is cumulative (and linear), and we adopt 34 weeks as an average antipsychotic prescription length then a crude multiplication of Schneider's 1% absolute risk of death gives a new NNH of 33. Applying this in turn to …
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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 | 0.008 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".