New Tests for the Diagnosis of Latent Tuberculosis Infection
Bibliographic record
Abstract
Letters6 November 2007New Tests for the Diagnosis of Latent Tuberculosis InfectionDick Menzies, MD, MSc and Madhukar Pai, MD, PhDDick Menzies, MD, MScFrom Montreal Chest Institute, McGill University, Montréal, Québec H2X 2P4, Canada.Search for more papers by this author and Madhukar Pai, MD, PhDFrom Montreal Chest Institute, McGill University, Montréal, Québec H2X 2P4, Canada.Search for more papers by this authorAuthor, Article, and Disclosure Informationhttps://doi.org/10.7326/0003-4819-147-9-200711060-00021 SectionsAboutFull TextPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinkedInRedditEmail IN RESPONSE:We agree with Drs. Kunst and Khan that the lack of a proper gold standard is a fundamental problem of all cross-sectional studies of diagnostic tests for latent tuberculosis infection. We state this problem explicitly several times in our paper. We believe that longitudinal studies following cohorts of persons with positive or negative test results will be most valuable, because the later development of active tuberculosis is the only certain indicator of the presence of latent tuberculosis infection. Because treatment reduces incidence of disease, ideally, such cohorts of individuals would be untreated, which poses serious ethical issues. However, ...References1. Pai M, Menzies D. The new IGRA and the old TST: making good use of disagreement [Editorial]. Am J Respir Crit Care Med. 2007;175:529-31. [PMID: 17341646] CrossrefMedlineGoogle Scholar2. Andersen P, Doherty TM, Pai M, Weldingh K. The prognosis of latent tuberculosis: can disease be predicted? Trends Mol Med. 2007;13:175-82. [PMID: 17418641] CrossrefMedlineGoogle Scholar3. Pai M, Dheda K, Cunningham J, Scano F, O'Brien R. T-cell assays for the diagnosis of latent tuberculosis infection: moving the research agenda forward. Lancet Infect Dis. 2007;7:428-38. [PMID: 17521596] CrossrefMedlineGoogle Scholar Author, Article, and Disclosure InformationAuthors: Dick Menzies, MD, MSc; Madhukar Pai, MD, PhDAffiliations: From Montreal Chest Institute, McGill University, Montréal, Québec H2X 2P4, Canada.Disclosures: None disclosed. PreviousarticleNextarticle Advertisement FiguresReferencesRelatedDetailsSee AlsoMeta-analysis: New Tests for the Diagnosis of Latent Tuberculosis Infection: Areas of Uncertainty and Recommendations for Research Dick Menzies , Madhukar Pai , and George Comstock New Tests for the Diagnosis of Latent Tuberculosis Infection Heinke Kunst and Khalid S. Khan Metrics Cited byIntestinal tuberculosis: clinico-pathological profile and the importance of a high degree of suspicion 6 November 2007Volume 147, Issue 9Page: 673-674KeywordsConflicts of interestHealth information technologyLongitudinal studiesPopulation statisticsPrevention, policy, and public healthProspective studiesSpecificitySystematic reviewsTuberculosis ePublished: 6 November 2007 Issue Published: 6 November 2007 Copyright & PermissionsCopyright © 2007 by American College of Physicians. All Rights Reserved.PDF downloadLoading ...
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.017 | 0.010 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".