Safer Prescribing and Care for the Elderly (SPACE): a pilot study in general practice
Bibliographic record
Abstract
BACKGROUND: High-risk prescribing places patients at increased risk of adverse drug events (ADEs). High-risk prescribing and ADE hospitalisations are increasingly common as people are living longer and taking more medicines for multiple chronic conditions. The Safer Prescribing and Care for the Elderly (SPACE) intervention is designed to foster patient engagement in medicines management and prompt medicines review. AIM: To pilot the SPACE intervention in preparation for a larger cluster randomised controlled trial (RCT). DESIGN & SETTING: A pilot study in two general practices. Study participants were all patients at increased risk of an adverse drug reaction (ADE) from non-steroidal anti-inflammatory drugs (NSAIDs) and/or antiplatelet medicines. The primary outcome was the proportion of participants receiving high-risk prescribing at 6 months and 12 months compared with baseline. METHOD: The SPACE intervention comprised automated practice audit to identify and generate for each GP a list of patients with high-risk prescribing for these medicines; an outreach visit by clinical advisory pharmacist to deliver education and to go through with each GP their list of at-risk patients and indicate in a tick-box the intended action for each patient; and a mail-out from GPs to selected patients containing a medicines information brochure and a letter encouraging patients to discuss their medicines when they next see their GP. RESULTS: SPACE can be delivered within existing primary care infrastructure. The rate of high-risk prescribing was reduced at 6 months following the delivery of the intervention, but these improvements were not evident at 12 months. CONCLUSION: SPACE prompts medicines review and shows promising signs of supporting safer prescribing in general practice in the short term. A randomised trial of SPACE started in 2018.
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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 | 0.011 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".