Evidence map›Paper›PMID 38283395›Full record

ArticleMDM policy & practice

Using Expert Elicitation to Adjust Published Intervention Effects to Reflect the Local Context.

Jodi Gray, Tilenka R Thynne, Vaughn Eaton, Rebecca Larcombe, Mahsa Tantiongco, Jonathan Karnon

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in MDM policy & practice. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Jodi GrayFlinders Health and Medical Research Institute (FHMRI), College of Medicine and Public Health, Flinders University, Bedford Park, SA, Australia.ORCID https://orcid.org/0000-0002-1119-7078
Tilenka R ThynneFlinders Medical Centre, Southern Adelaide Local Health Network (SALHN), Bedford Park, SA, Australia.
Vaughn EatonSA Pharmacy Southern Adelaide Local Health Network (SALHN), Department of Health and Wellbeing, SA Health, Government of South Australia, Bedford Park, SA, Australia.
Rebecca LarcombeSA Pharmacy Southern Adelaide Local Health Network (SALHN), Department of Health and Wellbeing, SA Health, Government of South Australia, Bedford Park, SA, Australia.
Mahsa TantiongcoSA Pharmacy Southern Adelaide Local Health Network (SALHN), Department of Health and Wellbeing, SA Health, Government of South Australia, Bedford Park, SA, Australia.
Jonathan KarnonFlinders Health and Medical Research Institute (FHMRI), College of Medicine and Public Health, Flinders University, Bedford Park, SA, Australia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Highlights: Local health services make limited use of economic evaluation to inform their decisions on the funding of new health service interventions. One barrier to use is the relevance of published intervention evaluations to the local setting.Expert elicitation methods provide a structured way to consider differences between the evaluation and local settings and to explicitly and transparently adjust published effect estimates for use in local economic evaluations.The pragmatic elicitation methods reported in this article offer a feasible and acceptable approach to adjusting published intervention effects to better reflect the effects expected in the local context. This increases the relevance of economic evaluations for local decision makers.

Indexed as

health services researchhospital-acquired complicationhypoglycemialocal health service evaluationstructured expert elicitation

Identifiers

PMID38283395
PMCPMC10812103

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.