Predictors of discordant latent tuberculosis infection test results amongst South African health care workers
Bibliographic record
Abstract
BACKGROUND: The tuberculin skin test (TST) and interferon-gamma-release-assays (IGRAs) are utilized in screening programmes for presumed latent tuberculosis infection (LTBI) in health care workers (HCWs). However, inter-test comparison yields high rates of discordance, which is poorly understood. The aim of the study was therefore to identify factors associated with discordance amongst HCWs in a TB and HIV endemic setting. METHODS: 505 HCWs were screened for LTBI in South Africa using the TST and two IGRA assays (QuantiFERON-TB-Gold-In-Tube (QFT-GIT) and TSPOT.TB). Factors associated with discordance were analyzed using a multinomial logistic regression model. RESULTS: TST-IGRA discordance was negatively associated with longer duration of employment for both TSPOT.TB (OR = 0.92; 95% confidence interval (CI) 0.85-0.99) and QFT-GIT (OR = 0.90; 95% CI 0.84-0.96). Marked test discordance occurred in HIV-infected individuals who were more likely to have TSPOT.TB + ve / TST-ve discordance (OR 4.44; 95% CI 1.14-17.27) or TSPOT.TB + ve / QFT-GIT-ve test discordance (OR 5.72; 95% CI 1.95-16.78). Those engaged in home care were less likely to have QFT-GIT + ve/TSPOT.TB -ve / discordance (OR 0.32; 95% CI 0.10-0.95). CONCLUSION: The marked TST-IGRA and IGRA-IGRA discordance in HIV-infected individuals suggest greater sensitivity of TSPOT.TB in immunocompromised persons or potential greater reactivity of TSPOT.TB in this population.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".