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Record W4399583434 · doi:10.51731/cjht.2024.916

Advisory Panel Guidance on Minimum Retesting Intervals for Lab Tests

2024· article· en· W4399583434 on OpenAlexfundaboutno aff
CADTH

Bibliographic record

VenueCanadian Journal of Health Technologies · 2024
Typearticle
Languageen
FieldHealth Professions
TopicHealthcare cost, quality, practices
Canadian institutionsnot available
FundersLupus CanadaStrongDiabetes CanadaJuvenile Diabetes Research Foundation Canada
KeywordsTest (biology)MedicineNatriuretic peptideFamily medicineMedical educationInternal medicine

Abstract

fetched live from OpenAlex

What Is the Issue? Lab test overuse can contribute to further unnecessary follow-up and testing, negative patient experiences, potentially inappropriate treatments, and the inefficient use of health care resources. One review of lab testing in Canada found that around 22% of blood tests were likely unnecessary. One strategy to address lab test overuse is to establish minimal retesting intervals that can be implemented in medical laboratories to help identify and manage potentially inappropriate lab test requests. Minimum retesting intervals suggest the minimum time before a test should be repeated based on the biochemical properties of the test and the clinical situation in which it is used. They are intended to inform clinical decisions about repeat testing. The importance of lab resource stewardship is being addressed by Choosing Wisely Canada through Using Labs Wisely, a consortium of more than 150 hospitals committed to driving the appropriate use of lab testing in Canada. The hospitals participating in Using Labs Wisely identified a need for guidance on minimum retesting intervals for commonly used lab tests. What Did We Do? Choosing Wisely Canada and CADTH partnered to convene an independent time-limited advisory panel to develop consensus-based recommendations for minimum retesting intervals for 7 commonly used lab tests (antinuclear antibody [ANA], B-type natriuretic peptide [BNP] and N-terminal pro b-type natriuretic peptide [NT-proBNP], Hemoglobin A1C, lipase, lipid panel, serum protein electrophoresis [SPEP], and thyroid stimulating hormone [TSH]) in prespecified patient populations. The advisory panel included core and specialist members who were recruited from across Canada. The 7 core advisory panel members brought together expertise in laboratory medicine, family practice, and patient lived experience. Seven additional specialist members brought expertise in endocrinology, cardiology, pediatric cardiology, rheumatology, hematology oncology, gastroenterology, and general internal medicine. The Advisory Panel on Minimum Retesting Intervals considered patient group input, evidence from focused literature reviews, equity considerations, and clinical expertise. Through facilitated discussion, they reached consensus on the recommendations for minimum retesting intervals. Following external feedback, the recommendations for BNP and NT-proBNP and lipid panels were removed, and this document includes recommendations for minimum retesting intervals for 5 lab tests. These are not recommendations for repeat testing. They are recommendations that if testing is undertaken, it should not be repeated sooner than the indicated intervals. They are not intended to replace clinical judgment as there may be exceptions in which the recommendations do not apply. What Is the Potential Impact? The recommendations on minimum retesting intervals can support the hospitals participating in Choosing Wisely Canada’s Using Labs Wisely program in their effort to reduce unnecessary lab tests and their impact on patients, providers, health systems, and the environment. The recommendations may also be relevant to community and hospital lab stewardship efforts and may address the appropriate use of the 5 lab tests by enabling changes in lab test ordering in both inpatient and outpatient settings.

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)0.010
metaresearch head score (Gemma)0.030
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.835
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0100.030
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.003
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 head0.677
GPT teacher head0.544
Teacher spread0.133 · 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
GenreEmpirical

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

Citations3
Published2024
Admission routes2
Has abstractyes

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