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Economic Analysis of a Randomized Trial of Academic Detailing Interventions to Improve Use of Antihypertensive Medications

2007· article· en· W1986655371 on OpenAlexaff
Steven R. Simon, Hector P. Rodríguez, Sumit R. Majumdar, Ken Kleinman, Cheryl K. Warner, Susanne Salem‐Schatz, Irina Miroshnik, Stephen B. Soumerai, Lisa A. Prosser

Bibliographic record

VenueJournal of Clinical Hypertension · 2007
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicHealth Systems, Economic Evaluations, Quality of Life
Canadian institutionsUniversity of Alberta
FundersAgency for Healthcare Research and Quality
KeywordsMedicineAcademic detailingPsychological interventionConfidence intervalGuidelineRandomized controlled trialIntervention (counseling)Cost–benefit analysisFamily medicineSurgeryInternal medicineNursing

Abstract

fetched live from OpenAlex

The authors estimated the costs and cost savings of implementing a program of mailed practice guidelines and single-visit individual and group academic detailing interventions in a randomized controlled trial to improve the use of antihypertensive medications. Analyses took the perspective of the payer. The total costs of the mailed guideline, group detailing, and individual detailing interventions were estimated at 1000 dollars, 5500 dollars, and 7200 dollars, respectively, corresponding to changes in the average daily per person drug costs of -0.0558 dollars (95% confidence interval, -0.1365 dollars to 0.0250 dollars) in the individual detailing intervention and -0.0001 dollars (95% confidence interval, -0.0803 dollars to 0.0801 dollars) in the group detailing intervention, compared with the mailed intervention. For all patients with incident hypertension in the individual detailing arm, the annual total drug cost savings were estimated at 21,711 dollars (95% confidence interval, 53,131 dollars savings to 9709 dollars cost increase). Information on costs of academic detailing could assist with health plan decision making in developing interventions to improve prescribing.

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 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)0.073
metaresearch head score (Gemma)0.065
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesMetaresearch
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Randomized trial · Consensus signal: Randomized trial
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.093
Threshold uncertainty score0.955

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0730.065
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0060.003
Bibliometrics0.0020.001
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 head0.539
GPT teacher head0.543
Teacher spread0.005 · 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

Classification

machine, unvalidated

Machine predicted; both teacher heads agree on what is shown here.

Study designRandomized trial
Domainnot available
GenreEmpirical

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".

Quick stats

Citations34
Published2007
Admission routes1
Has abstractyes

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