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Record W4323924096 · doi:10.1186/s13104-023-06294-2

Insights from using an outcomes measurement properties search filter and conducting citation searches to locate psychometric articles of tools used to measure context attributes

2023· article· en· W4323924096 on OpenAlexafffund
Wilmer J. Santos, Alison M. Hutchinson, Tamara Rader, Ian D. Graham, Vanessa Watkins, Ligyana Korki de Cândido, Megan Greenough, Janet E. Squires

Bibliographic record

VenueBMC Research Notes · 2023
Typearticle
Languageen
FieldDecision Sciences
TopicMeta-analysis and systematic reviews
Canadian institutionsUniversity of OttawaCanadian Agency for Drugs and Technologies in HealthOttawa Hospital
FundersCanadian Institutes of Health ResearchBarwon Health Foundation
KeywordsMeasure (data warehouse)Context (archaeology)CitationData scienceComputer scienceInformation retrievalFilter (signal processing)MedicineWorld Wide WebData miningBiology

Abstract

fetched live from OpenAlex

OBJECTIVES: To describe our experience with using a methodological outcomes measurement search filter (precise and sensitive versions of a filter designed to locate articles that report on psychometric properties of measurement tools) and citation searches to locate psychometric articles for tools that can be used to measure context attributes. To compare the precise filter when used alone and with reference list checking to citation searching according to number of records found, precision, and sensitivity. RESULTS: Using the precise filter, we located 130 of 150 (86.6%) psychometric articles related to 22 of 31 (71.0%) tools that potentially measured an attribute of context. In a subset of six tools, the precise filter alone was more precise than searching with the precise filter combined with reference list searching, or citation searching alone. The precise filter combined with reference list checking was the most sensitive search method examined. Overall, we found the precise filter helpful for our project as it decreased record screening time. For non-patient reported outcomes tools, we had less success with locating psychometric articles using the precise filter because some psychometric articles were not indexed in PubMed. More research that systematically evaluates database searching methods is needed to validate our findings.

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

Direct model labels (unvalidated)

Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.

Model armCategoriesStudy designConfidence
gemmaMetaresearchBibliometrics
Domain: Methods · Genre: Empirical
About the Canadian research system: no · About a Canadian topic: no
Observationalmedium
gptMetaresearch
Domain: Methods · Genre: Methods
About the Canadian research system: no · About a Canadian topic: no
Other designlow
models splitAgreement compares identical category sets and study designs across arms.

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)0.119
metaresearch head score (Gemma)0.233
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Scholarly communication, Insufficient payload (model declined to judge)
Consensus categoriesMetaresearch
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.281
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.1190.233
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0030.010
Science and technology studies0.0000.000
Scholarly communication0.0020.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.001

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 head0.992
GPT teacher head0.583
Teacher spread0.409 · 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

Labeled directly by 2 models reading the full record.

MetaresearchBibliometrics

The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.

Study designObservational · Other design
DomainMethods
GenreEmpirical · Methods

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

Citations0
Published2023
Admission routes2
Has abstractyes

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