Tuberculin Skin Testing Is Useful in the Screening for Nontuberculous Mycobacterial Cervicofacial Lymphadenitis in Children
Bibliographic record
Abstract
BACKGROUND: We evaluated the diagnostic usefulness of tuberculin skin testing in the screening for nontuberculous mycobacterial (NTM) infection in children. METHODS: We enrolled 180 children who had chronic cervicofacial lymphadenitis in our study. Skin testing was done using antigens of Mycobacterium tuberculosis, Mycobacterium avium, Mycobacterium kansasii, and Mycobacterium scrophulaceum. The reference standard for NTM infection was a positive culture result, identification by PCR, or both. Receiver operating characteristic analysis was used to identify the optimal cutoff point in skin induration for the detection of NTM infection. Accuracy of the mycobacterial skin tests was quantified using sensitivity and specificity rates and positive and negative predictive values at the optimal skin induration cutoff. RESULTS: A total of 112 NTM infections were identified, of which 83 were caused by M. avium, 21 by Mycobacterium haemophilum, and 8 by other NTM species. At the optimal cutoff for a positive test (5 mm), tuberculin skin testing had a sensitivity and specificity of 70% and 98%, respectively, and a positive predictive value and a negative predictive value of 98% and 64%, respectively, compared with a sensitivity and a specificity of 93% and 97%, respectively; M. avium sensitin, the best-performing skin test, had positive and negative predictive values of 98% and 90%, respectively. CONCLUSION: Tuberculin skin testing could be valuable as a first step in the diagnostic analysis of cervicofacial lymphadenitis in children without a history of TB exposure or bacille Calmette-Guérin vaccination.
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 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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".