ArticleBMC primary care2022
Influence of data disclosures on physician decisions about off-label uses: findings from a qualitative study.
Article in BMC primary care, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
3 citing papers in PubMed, 3 citations in OpenAlex.
- Industry Interactions at Medical Conferences: Representatives' Credentials and the Disclosure of Data Limitations Influence Clinician Perceptions.Journal of the American Board of Family Medicine : JABFM · 2025Trial
- Physician Perceptions and Intentions in Response to Unsupportive Data for Off-label Uses: An Experimental Study with Primary Care Providers and Oncologists.Therapeutic innovation & regulatory science · 2026Article
- Determinants of off-label drug acceptance in pregnancy: insights from an extended UTAUT model.Frontiers in pharmacology · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundPrescribing approved products for unapproved uses (off-label use) is not uncommon among physicians in certain medical specialties. Available evidence about an off-label use - both supportive and unsupportive - can influence prescribers' decisions about a drug's appropriateness for a particular case. The objectives of this study were: (1) to examine physician perceptions about off-label uses generally, including their awareness of unsupportive data; and (2) to explore the influence of disclosure information about unsupportive data on off-label prescribing decisions.
methodsSemi-structured interviews were conducted between December 2019 and January 2020 with oncologists (n = 35) and primary care physicians (n = 35). Interviews explored general prescribing practices, understanding of and information sources for learning about off-label use of prescription drugs, awareness of unsupportive data related to off-label uses, and preferences and reactions to disclosure statements about the existence of unsupportive data related to an off-label use.
resultsMost participants reported prescribing drugs for off-label uses (with half reporting regular off-label prescribing). However, among those who prescribe off-label, approximately two-thirds had never seen unsupportive data about off-label uses. Physicians preferred a disclosure statement that provided a summary of the unsupportive data about the off-label use; this statement also led most physicians to say they were unlikely or less likely to prescribe the drug for that use.
conclusionsThis study suggests that physicians' decision-making about prescribing for off-label uses of approved drugs may be influenced by awareness of unsupportive data. Our interviews also suggest that providing more information about unsupportive study findings may result in a reduction in reported prescribing likelihood.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.