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When and how to establish a new reference standard for medical tests: a scoping review identifying methodological priorities

2025· review· en· W4417068344 on OpenAlexaff
Sam White, Miranda Langendam, Bada Yang, Yasaman Vali, Yaxin Chen, Elio Arruzza, Minh Chau, Mason Henry Crossman, Zachary Munn, Tracy Merlin, Timothy Hugh Barker, Holger J. Schünemann

Bibliographic record

VenueJournal of Clinical Epidemiology · 2025
Typereview
Languageen
FieldMedicine
TopicClinical Laboratory Practices and Quality Control
Canadian institutionsMcMaster University
Fundersnot available
KeywordsReference dataTest (biology)WorkflowGold standard (test)GuidelineReference modelReference valuesHealth care

Abstract

fetched live from OpenAlex

BACKGROUND AND OBJECTIVE: The reference standard, defined as the best available test or test strategy for diagnosing a target disease/condition in a specific clinical population, is rarely perfectly accurate. This imperfection raises the question of when (and how) to establish a new test as the reference standard test. The objective of this scoping review was to assess methodological priorities for establishing a new reference standard for medical tests. METHODS: This scoping review was performed using JBI methodology and reported in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-analyses extension for scoping reviews reporting guideline. We included sources describing methodological approaches to establishing a new reference standard across all healthcare populations and settings. Data were analyzed using a combination of descriptive and qualitative content and thematic analyses. RESULTS: We screened 5396 studies and included 13 in our final analysis (7 methodological studies and 6 applied studies). Three main methodological themes emerged: (1) demonstrating the imperfectness of the current reference standard, (2) comparing candidate reference standard tests, and (3) decision principles for accepting a new reference standard. Among the six applied studies that established reference standards for specific conditions, five used composite reference standards. CONCLUSION: Based on the findings of our review, we define a potential workflow for structuring decision-making relating to whether to accept a new test as the reference standard. Development of formal methodological guidance for when and how to establish or replace a reference standard would likely benefit guideline developers, clinicians, technology assessors, and, ultimately, patients. PLAIN LANGUAGE SUMMARY: "Reference standard" tests are considered the best available method to determine whether someone has a particular health condition. However, these tests are often imperfect, and there is little guidance on when a new test should replace the current reference standard. We reviewed published research to understand how new reference standards have been created or evaluated. Thirteen studies met our search criteria. Together, they showed four key steps: first, defining the purpose of the reference standard (for clinical care or research); second recognizing when the current reference standard is imperfect; third, comparing potential new tests; and fourth, deciding whether adopting a new test will improve patient care. Our findings highlight the need for formal guidance to support groups creating new reference standards in the future.

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)0.165
metaresearch head score (Gemma)0.916
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Meta-epidemiology (broad), Research integrity
Consensus categoriesMetaresearch, Research integrity
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.828
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.1650.916
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0250.003
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0020.004
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 head0.817
GPT teacher head0.699
Teacher spread0.118 · 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; both teacher heads agree on what is shown here.

Study designOther design
Domainnot available
GenreReview

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
Published2025
Admission routes1
Has abstractyes

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