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Methodological approaches for developing and reporting living evidence synthesis: a study protocol

2022· article· en· W4221047172 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueOpen Research Europe · 2022
Typearticle
Languageen
FieldDecision Sciences
TopicMeta-analysis and systematic reviews
Canadian institutionsMcMaster University
FundersHorizon 2020 Framework ProgrammeEuropean Commission
KeywordsProtocol (science)Systematic reviewMEDLINEData scienceComputer scienceEvidence-based medicineManagement sciencePsychologyMedicineAlternative medicinePolitical sciencePathology

Abstract

fetched live from OpenAlex

<ns4:p> <ns4:bold>Background</ns4:bold> : Living evidence (LE) refers to the methodological processes that permit new research findings to be continually incorporated into evidence synthesis. This approach is of great value in the resolution of relevant and rapidly changing clinical questions. To date, the methods to carry out this type of synthesis are not completely defined, and great variability is observed in the approaches used by different groups of authors. </ns4:p> <ns4:p> <ns4:bold>Objective:</ns4:bold> To identify, evaluate and summarise the current methods used for living evidence synthesis </ns4:p> <ns4:p> <ns4:bold>Methods: </ns4:bold> We will conduct a methodological study based on a systematic literature search to identify any type of evidence synthesis such as systematic reviews, network metanalyses and overviews that used “living evidence synthesis” as part of their methods. The search will be conducted in Medline (via PubMed) and Epistemonikos databases. Additionally, we will search websites of the organisations publishing any living evidence synthesis retrieved in the two databases, in order to identify unpublished subsequent reports. Two reviewers will independently assess each article against the selection criteria, extract data on methods and procedures, and assess the methodological quality of each publication. Data will be analysed descriptively. </ns4:p>

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.

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)0.813
metaresearch head score (Gemma)0.857
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Science and technology studies, Scholarly communication, Open science, Insufficient payload (model declined to judge)
Consensus categoriesMetaresearch
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Protocol · Consensus signal: Protocol
Teacher disagreement score0.670
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.8130.857
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0030.000
Bibliometrics0.0000.003
Science and technology studies0.0020.000
Scholarly communication0.0050.000
Open science0.0050.008
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.995
GPT teacher head0.750
Teacher spread0.244 · 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