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Record W2176000419 · doi:10.1370/afm.1838

Effectiveness of a Multifaceted Intervention for Potentially Inappropriate Prescribing in Older Patients in Primary Care: A Cluster-Randomized Controlled Trial (OPTI-SCRIPT Study)

2015· article· en· W2176000419 on OpenAlex
Barbara Clyne, Susan M. Smith, Carmel Hughes, Fiona Boland, Marie C. Bradley, J Cooper, Tom Fahey

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

fundA Canadian funder is recorded on the work.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueThe Annals of Family Medicine · 2015
Typearticle
Languageen
FieldMedicine
TopicPharmaceutical Practices and Patient Outcomes
Canadian institutionsnot available
FundersQueen's University BelfastQueen's UniversityRoyal College of Surgeons in Ireland
KeywordsMedicineRandomized controlled trialCluster (spacecraft)Intervention (counseling)Primary careCluster randomised controlled trialFamily medicineNursingInternal medicine

Abstract

fetched live from OpenAlex

PURPOSE: Potentially inappropriate prescribing (PIP) is common in older people and can result in increased morbidity, adverse drug events, and hospitalizations. The OPTI-SCRIPT study (Optimizing Prescribing for Older People in Primary Care, a cluster-randomized controlled trial) tested the effectiveness of a multifaceted intervention for reducing PIP in primary care. METHODS: We conducted a cluster-randomized controlled trial among 21 general practitioner practices and 196 patients with PIP. Intervention participants received a complex, multifaceted intervention incorporating academic detailing; review of medicines with web-based pharmaceutical treatment algorithms that provide recommended alternative-treatment options; and tailored patient information leaflets. Control practices delivered usual care and received simple, patient-level PIP feedback. Primary outcomes were the proportion of patients with PIP and the mean number of potentially inappropriate prescriptions. We performed intention-to-treat analysis using random-effects regression. RESULTS: All 21 practices and 190 patients were followed. At intervention completion, patients in the intervention group had significantly lower odds of having PIP than patients in the control group (adjusted odds ratio = 0.32; 95% CI, 0.15-0.70; P = .02). The mean number of PIP drugs in the intervention group was 0.70, compared with 1.18 in the control group (P = .02). The intervention group was almost one-third less likely than the control group to have PIP drugs at intervention completion, but this difference was not significant (incidence rate ratio = 0.71; 95% CI, 0.50-1.02; P = .49). The intervention was effective in reducing proton pump inhibitor prescribing (adjusted odds ratio = 0.30; 95% CI, 0.14-0.68; P = .04). CONCLUSIONS: The OPTI-SCRIPT intervention incorporating academic detailing with a pharmacist, and a review of medicines with web-based pharmaceutical treatment algorithms, was effective in reducing PIP, particularly in modifying prescribing of proton pump inhibitors, the most commonly occurring PIP drugs nationally.

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.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.010
metaresearch head score (Gemma)0.006
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Randomized trial · Consensus signal: Randomized trial
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.049
Threshold uncertainty score0.765

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0100.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.254
GPT teacher head0.450
Teacher spread0.196 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it