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Record W2236349991 · doi:10.1128/jcm.02803-15

Interferon Gamma Release Assays for Latent Tuberculosis: What Are the Sources of Variability?

2016· review· en· W2236349991 on OpenAlex
Niaz Banaei, Rajiv L. Gaur, Madhukar Pai

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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueJournal of Clinical Microbiology · 2016
Typereview
Languageen
FieldMedicine
TopicTuberculosis Research and Epidemiology
Canadian institutionsMcGill University
FundersCanadian Institutes of Health Research
KeywordsLatent tuberculosisInterferon gammaTuberculosisInterferon gamma release assayMycobacterium tuberculosisVirologyBiologyMicrobiologyImmunologyMedicineCytokinePathology

Abstract

fetched live from OpenAlex

Interferon gamma release assays (IGRAs) are blood-based tests intended for diagnosis of latent tuberculosis infection (LTBI). IGRAs offer logistical advantages and are supposed to offer improved specificity over the tuberculin skin test (TST). However, recent serial testing studies of low-risk individuals have revealed higher false conversion rates with IGRAs than with TST. Reproducibility studies have identified various sources of variability that contribute to nonreproducible results. Sources of variability can be broadly classified as preanalytical, analytical, postanalytical, manufacturing, and immunological. In this minireview, we summarize known sources of variability and their impact on IGRA results. We also provide recommendations on how to minimize sources of IGRA variability.

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)0.017
metaresearch head score (Gemma)0.039
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: Review · Consensus signal: Review
Teacher disagreement score0.963
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0170.039
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0070.004
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.002
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 head0.134
GPT teacher head0.457
Teacher spread0.323 · 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