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Record W3204390088 · doi:10.21203/rs.3.rs-931213/v1

Identifying and addressing conflicting results across multiple discordant systematic reviews on the same topic: A protocol for a replication study of the Jadad algorithm

2021· preprint· en· W3204390088 on OpenAlexafffund
Carole Lunny, Sai Surabi Thirugnanasampanthar, Salman Kanji, Nicola Ferri, Dawid Pieper, Sera Whitelaw, Pierre Thabet, Sara Tasmin, Harrison Nelson, Emma K. Reid, Jia He Zhang, Banveer Kalkat, Yuan Chi, Jacqueline Thompson, Reema Abdoulrezzak, Di Wen Zheng, Lindy R.S. Pangka, Dian Wang, Parisa Safavi, Anmol Sooch, Kevin T. Kang, Andrea C. Tricco

Bibliographic record

VenueResearch Square · 2021
Typepreprint
Languageen
FieldDecision Sciences
TopicMeta-analysis and systematic reviews
Canadian institutionsUniversity of British ColumbiaNova Scotia Health AuthorityMontfort HospitalMcMaster UniversityQueen's UniversityImpactOttawa Hospital
FundersOntario Ministry of Research and InnovationCanadian Institutes of Health Research
KeywordsReplication (statistics)Protocol (science)Jadad scaleComputer scienceAlgorithmBiologyMathematicsMedicineMEDLINEStatisticsAlternative medicine

Abstract

fetched live from OpenAlex
No abstract in any covered source. Its absence is recorded, not treated as a negative.

No abstract. This is not a gap in this database; OpenAlex has none either. 23.3% of the frame is in this state, and the screen finds HALF as much metaresearch here, so the absence is a measured bias rather than a missing field.

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)0.305
metaresearch head score (Gemma)0.552
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesMetaresearch
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Protocol · Consensus signal: Protocol
Teacher disagreement score0.695
Threshold uncertainty score0.857

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.3050.552
Meta-epidemiology (narrow)0.0060.008
Meta-epidemiology (broad)0.0090.016
Bibliometrics0.0100.010
Science and technology studies0.0060.006
Scholarly communication0.0070.006
Open science0.0050.009
Research integrity0.0130.014
Insufficient payload (model declined to judge)0.0420.012

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 head0.925
GPT teacher head0.694
Teacher spread0.231 · 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 designNot applicable
DomainMethods
GenreProtocol

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

Citations1
Published2021
Admission routes2
Has abstractno

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