Evidence map›Paper›PMID 29333105›Full record

ReviewPoint of care2017

Measures and Metrics for Feasibility of Proof-of-Concept Studies With Human Immunodeficiency Virus Rapid Point-of-Care Technologies: The Evidence and the Framework.

Nitika Pant Pai, Tiago Chiavegatti, Rohit Vijh, Nicolaos Karatzas, Jana Daher, Megan Smallwood, Tom Wong, Nora Engel

Abstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in Point of care, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing 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

6 citing papers in PubMed.

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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

8 authors.

Nitika Pant PaiDepartment of Medicine, McGill University; †Division of Clinical Epidemiology, McGill University Health Centre, Montreal, Quebec; ‡Dalla Lana School of Public Health, University of Toronto, Canada; and §Department of Health, Ethics and Society, Research School for Public Health and Primary Care, Maastricht University, Maastricht, The Netherlands.
Tiago ChiavegattiDepartment of Medicine, McGill University; †Division of Clinical Epidemiology, McGill University Health Centre, Montreal, Quebec; ‡Dalla Lana School of Public Health, University of Toronto, Canada; and §Department of Health, Ethics and Society, Research School for Public Health and Primary Care, Maastricht University, Maastricht, The Netherlands.
Rohit VijhDepartment of Medicine, McGill University; †Division of Clinical Epidemiology, McGill University Health Centre, Montreal, Quebec; ‡Dalla Lana School of Public Health, University of Toronto, Canada; and §Department of Health, Ethics and Society, Research School for Public Health and Primary Care, Maastricht University, Maastricht, The Netherlands.
Nicolaos KaratzasDepartment of Medicine, McGill University; †Division of Clinical Epidemiology, McGill University Health Centre, Montreal, Quebec; ‡Dalla Lana School of Public Health, University of Toronto, Canada; and §Department of Health, Ethics and Society, Research School for Public Health and Primary Care, Maastricht University, Maastricht, The Netherlands.
Jana DaherDepartment of Medicine, McGill University; †Division of Clinical Epidemiology, McGill University Health Centre, Montreal, Quebec; ‡Dalla Lana School of Public Health, University of Toronto, Canada; and §Department of Health, Ethics and Society, Research School for Public Health and Primary Care, Maastricht University, Maastricht, The Netherlands.
Megan SmallwoodDepartment of Medicine, McGill University; †Division of Clinical Epidemiology, McGill University Health Centre, Montreal, Quebec; ‡Dalla Lana School of Public Health, University of Toronto, Canada; and §Department of Health, Ethics and Society, Research School for Public Health and Primary Care, Maastricht University, Maastricht, The Netherlands.
Tom WongDepartment of Medicine, McGill University; †Division of Clinical Epidemiology, McGill University Health Centre, Montreal, Quebec; ‡Dalla Lana School of Public Health, University of Toronto, Canada; and §Department of Health, Ethics and Society, Research School for Public Health and Primary Care, Maastricht University, Maastricht, The Netherlands.
Nora EngelDepartment of Medicine, McGill University; †Division of Clinical Epidemiology, McGill University Health Centre, Montreal, Quebec; ‡Dalla Lana School of Public Health, University of Toronto, Canada; and §Department of Health, Ethics and Society, Research School for Public Health and Primary Care, Maastricht University, Maastricht, The Netherlands.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectivePilot (feasibility) studies form a vast majority of diagnostic studies with point-of-care technologies but often lack use of clear measures/metrics and a consistent framework for reporting and evaluation. To fill this gap, we systematically reviewed data to (

methodsFor the period January 2000 to March 2014, 2 reviewers searched 4 databases (MEDLINE, EMBASE, CINAHL, Scopus), retrieved 1441 citations, and abstracted data from 81 studies. We observed 2 major categories of measures, that is, implementation centered and patient centered, and 4 subcategories of measures, that is, feasibility, acceptability, preference, and patient experience. We defined and delineated metrics and measures for a feasibility framework. We documented impact measures for a comparison.

findingsWe observed heterogeneity in reporting of metrics as well as misclassification and misuse of metrics within measures. Although we observed poorly defined measures and metrics for feasibility, preference, and patient experience, in contrast, acceptability measure was the best defined. For example, within feasibility, metrics such as consent, completion, new infection, linkage rates, and turnaround times were misclassified and reported. Similarly, patient experience was variously reported as test convenience, comfort, pain, and/or satisfaction. In contrast, within impact measures, all the metrics were well documented, thus serving as a good baseline comparator. With our framework, we classified, delineated, and defined quantitative measures and metrics for feasibility.

conclusionsOur framework, with its defined measures/metrics, could reduce misclassification and improve the overall quality of reporting for monitoring and evaluation of rapid point-of-care technology strategies and their context-driven optimization.

Indexed as

feasibilityframeworkmeasuresmetricspoint-of-care

Identifiers

PMID29333105
PMCPMC5737458

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.