Concordance between IGRA and TST in a cohort of health professional trainees from India
Bibliographic record
Abstract
Objectives: To estimate the concordance between the Tuberculin skin test (TST) and Quantiferon TB Gold In-tube, (QFT) among health professional trainees at a referral hospital in India, and to evaluate risk factors associated with discordant results. Methods: From November 2009 to February 2011, students registered in various health professional programs (with the exception of medical and nursing students) were approached to participate. In addition to a questionnaire on TB exposure, participants underwent TST (10 mm cutoff) and the QFT-GIT (0.35 IU/ml cut off). Results: 164 students completed. Mean age was 21.5 yrs (Range: 17-34), 48.8% were female and 59.15% had BCG scars. Mean time in health care was 11.5 months, and 21.6% recalled direct contact with PTB. Prevalence of LTBI by TST or QFT was 48.2% and 23.2% respectively. Agreement between tests was 71.3% (kappa=0.415). The predominant discordance was TST +/QFT- (43/164, 26.2%). Using multivariate logistic regression we evaluated whether discordant results were associated with any particular risk factors, including: age, sex, education, family income, time in health care setting, days spent in high risk wards, performing high risk procedures, pre-existing medical illness, BCG, and known TB exposure. No factors were associated with discordant results, however, age was associated with concordant positives (OR=1.27, 95%CI: 1.03-1.59), and higher family income was protective (OR=0.67, 95%CI:0.45-0.99). Conclusions: There was fair to weak agreement between TST and QFT in this population. Concordant positives were associated with older age, and lower family income. Discordant results were not associated with any known risk factors.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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".