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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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.017
metaresearch head score (Gemma)0.016
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.695
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0170.016
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreOther

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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