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Record W4252813111 · doi:10.23970/ahrqepcmethguide1

Prioritization and Selection of Harms for Inclusion in Systematic Reviews

2017· report· en· W4252813111 on OpenAlexaff
Roger Chou, William L. Baker, Lionel L. Bañez, Suchitra Iyer, Evan R. Myers, Sydne J Newberry, Laura Pincock, Karen A. Robinson, Lyndzie Sardenga, Nila A Sathe, Stacey Springs, Timothy J Wilt

Bibliographic record

Venuenot available
Typereport
Languageen
FieldSocial Sciences
TopicDelphi Technique in Research
Canadian institutionsUniversity of Alberta
FundersVanderbilt UniversityAgency for Healthcare Research and QualityJohns Hopkins UniversityU.S. Department of Health and Human Services
KeywordsPrioritizationSelection (genetic algorithm)Inclusion (mineral)Systematic reviewComputer scienceActuarial scienceRisk analysis (engineering)Data scienceManagement sciencePsychologyBusinessPolitical scienceEconomicsArtificial intelligenceMEDLINESocial psychologyLaw

Abstract

fetched live from OpenAlex

Introduction Guidance from within the Agency for Healthcare Research and Quality’s (AHRQ) Evidence-based Practice Center (EPC) Program has long recognized the need for systematic reviews of interventions impacting health to provide balanced assessments that include evaluation of harms as well as benefits.

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)0.422
metaresearch head score (Gemma)0.675
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesMetaresearch
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.578
Threshold uncertainty score0.713

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.4220.675
Meta-epidemiology (narrow)0.0040.003
Meta-epidemiology (broad)0.0090.014
Bibliometrics0.0420.027
Science and technology studies0.0040.003
Scholarly communication0.0110.010
Open science0.0040.013
Research integrity0.0060.005
Insufficient payload (model declined to judge)0.0130.002

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 head0.332
GPT teacher head0.557
Teacher spread0.225 · 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
GenreMethods

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

Citations2
Published2017
Admission routes1
Has abstractyes

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