Bibliographic record
Abstract
Aims: Communication of hospital deprescribing decisions to the general practitioner (GP) is key for sustaining inpatientdeprescribing decisions into the community. This study aims to refine previously suggested language options to develop a preferred language and format to communicate this information in discharge summaries. Methods: Interviews and focus groups were conducted with 30 multidisciplinary clinicians, including seven GPs, eight pharmacists and 15 hospital doctors. Participants were presented with 10 case scenarios of deprescribing in older inpatients along with phrasing options previously suggested by clinicians. Participants were asked to nominate a preference, reasons why, suggested alterations, and discuss priorities, specific wording and location in the discharge summary. Final preferred language and format were developed using thematic content analysis and determined by consensus. Results: Participants reported the importance of structured phrasing to communicate the decision and plan in a GP followup section of the discharge summary. This format may facilitate further discussion in the GP practice. Participants preferred that deprescribing decisions be communicated using the following framework: 'Medication: Intention, Rationale. Clear plan (dose, duration, follow up). Patient agreement.' The cohort gave mixed responses about including information on the Drug Burden Index, monitoring or alternative management strategies. Using our results, a final 'fill-in-the-blank' template has been developed, reviewed by local geriatricians, and integrated into point-of-care guidelines for further validation. Conclusions: A structured preferred language guide for deprescribing decisions made in hospital for the discharge summary has been developed based on clinician preferences and expert consensus, and is undergoing further evaluation in practice.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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; both teacher heads agree on what is shown here.
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".