Bibliographic record
Abstract
Transitions of care are potentially dangerous times for patients as they travel through the health care system.Nowhere is this more evident than on discharge from hospital.Over an 11-month period about a decade ago, the Canadian Institute for Health Information found that hospital readmission within 30 days of initial discharge was common (estimated at 8.5%), costly (at $1.8 billion), and preventable more than half of the time. 1 Medication-related issues have been identified as an important contributor to readmissions, with prescribing problems and adherence issues most frequently cited. 2 Three papers in this issue highlight the discharge process, each emphasizing an important message.First, discharge planning needs to start early during hospital admission and should include a comprehensive medication review to prevent inappropriate prescribing on discharge, which in turn has the potential to reduce drug-related readmissions.Second, patient perspectives should be considered in discharge planning, something that is all too often neglected in our overwhelmed health care system these days.Finally, pharmacists need to critically think about how and when we perform evidence-based discharge activities.The need for comprehensive medication review as a component of early discharge planning is demonstrated by Madey and others. 3These authors identified prescribing issues that can arise for older, frail persons who receive antipsychotic agents during hospitalization.For one-third of the patients, these drugs were continued at discharge, with only 15% having a documented postdischarge antipsychotic follow-up plan and 40% still having an antipsychotic prescription at 180 days after discharge, putting them at risk of well-documented medication-related harms.While there are valid indications for antipsychotic use in older persons, these agents are not generally required over the long term when used for delirium, a common reason for prescribing antipsychotics during hospitalization.Moreover, when used for behaviours related to dementia, a plan for reassessment at 3 months should be in place. 4adey and others 3 identify comprehensive medication review as one means to ensure that antipsychotics prescribed in hospital are not continued inappropriately on discharge.This approach is consistent with guidance from the World Health Organization, which has identified
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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 | 0.002 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.005 | 0.013 |
| Insufficient payload (model declined to judge) | 0.044 | 0.022 |
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".