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Record W2589073600 · doi:10.1186/s13643-017-0425-7

Fit for purpose: perspectives on rapid reviews from end-user interviews

2017· article· en· W2589073600 on OpenAlexafffund
Lisa Hartling, Jeanne‐Marie Guise, Susanne Hempel, Robin Featherstone, Matthew D. Mitchell, Makalapua Motu’apuaka, Karen A. Robinson, Karen M Schoelles, Annette M Totten, Evelyn P Whitlock, Timothy J Wilt, Johanna Anderson, Elise Berliner, Aysegul Gozu, Elisabeth Kato, Robin Paynter, Craig A. Umscheid

Bibliographic record

VenueSystematic Reviews · 2017
Typearticle
Languageen
FieldDecision Sciences
TopicMeta-analysis and systematic reviews
Canadian institutionsUniversity of Alberta
FundersUniversity of AlbertaUniversity of Pennsylvania Health SystemUniversity of PennsylvaniaJohns Hopkins UniversityAgency for Healthcare Research and QualityU.S. Department of Health and Human Services
KeywordsMedicineInterimLimitingRelevance (law)GuidelineSystematic reviewQuality (philosophy)Evidence-based medicineEmpirical evidenceHealth careKey (lock)Medical educationKnowledge managementMEDLINEAlternative medicineComputer sciencePathology

Abstract

fetched live from OpenAlex

BACKGROUND: There is increasing demand for rapid reviews and timely evidence synthesis. The goal of this project was to understand end-user perspectives on the utility and limitations of rapid products including evidence inventories, rapid responses, and rapid reviews. METHODS: Interviews were conducted with key informants representing: guideline developers (n = 3), health care providers/health system organizations (n = 3), research funders (n = 1), and payers/health insurers (n = 1). We elicited perspectives on important characteristics of systematic reviews, acceptable methods to streamline reviews, and uses of rapid products. We analyzed content of the interview transcripts and identified themes and subthemes. RESULTS: Key informants identified the following as critical features of evidence reviews: (1) originating from a reliable source (i.e., conducted by experienced reviewers from an established research organization), (2) addressing clinically relevant questions, and (3) trusted relationship between the user and producer. Key informants expressed strong preference for the following review methods and characteristics: use of evidence tables, quality rating of studies, assessments of total evidence quality/strength, and use of summary tables for results and conclusions. Most acceptable trade-offs to increase efficiencies were limiting the literature search (e.g., limiting search dates or language) and performing single screening of citations and full texts for relevance. Key informants perceived rapid products (particularly evidence inventories and rapid responses) as useful interim products to inform downstream investigation (e.g., whether to proceed with a full review or guideline, direction for future research). Most key informants indicated that evidence analysis/synthesis and quality/strength of evidence assessments were important for decision-making. They reported that rapid reviews in particular were useful for guideline development on narrow topics, policy decisions when a quick turn-around is needed, decision-making for practicing clinicians in nuanced clinical settings, and decisions about coverage by payers/health insurers. Rapid reviews may be more relevant within specific clinical settings or health systems; whereas, broad/national guidelines often need a traditional systematic review. CONCLUSIONS: Key informants interviewed in our study indicated that evidence inventories, rapid responses, and rapid reviews have utility in specific decisions and contexts. They indicated that the credibility of the review producer, relevance of key questions, and close working relationship between the end-user and producer are critical for any rapid product. Our findings are limited by the sample size which may have been too small to reach saturation for the themes described.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.372
metaresearch head score (Gemma)0.517
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesMetaresearch
DomainCandidate signal: Methods · Consensus signal: Methods
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.628
Threshold uncertainty score0.775

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.3720.517
Meta-epidemiology (narrow)0.0020.003
Meta-epidemiology (broad)0.0020.003
Bibliometrics0.0050.004
Science and technology studies0.0160.030
Scholarly communication0.0300.033
Open science0.0060.032
Research integrity0.0170.022
Insufficient payload (model declined to judge)0.0070.003

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 head0.792
GPT teacher head0.557
Teacher spread0.235 · 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.

Study designQualitative
DomainMethods
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

Citations73
Published2017
Admission routes2
Has abstractyes

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