Comparison of different methods and times for reading the tuberculin skin test
Bibliographic record
Abstract
BACKGROUND: Current guidelines vary on the recommended method and time for measuring tuberculin skin test (TST) indurations. OBJECTIVE: To evaluate the best time and method for assessing TST results and which purified protein derivative (PPD) to administer. DESIGN: Standard PPD (PPD-S) and PPD-RT23 were applied concurrently on each forearm in random order in 78 nurses. MEASUREMENTS: TST induration was measured at 48, 72 and 96 h by two nurses by palpation and a ruler, palpation and a Vernier caliper, ballpoint pen and a ruler or ballpoint pen and a Vernier caliper. TST differences were assessed using mixed-effects analysis. We also assessed the rate of false-positive/-negative results and the variability of the TST measurements. RESULTS: We performed 767 TST measurements. The adjusted mean TST size was larger with PPD-S than with PPD-RT23 (12.8 vs. 10.8 mm, P < 0.001), and at 72 h than at 48 h and 96 h (13.4 vs. 11.8 vs. 10.1 mm, P < 0.05). The smallest number of false results was observed with PPD-S, the ballpoint pen-ruler and at 72 h; palpation+ruler had the least variability at 72 h. CONCLUSIONS: The TST should ideally be performed with PPD-S and measured at 72 h with the ballpoint pen+ruler or palpation+ruler methods.
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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 | 0.018 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".