A simple protocol for tuberculin skin test reading certification
Bibliographic record
Abstract
Although tuberculosis preventive therapy is one of the cornerstones for eliminating the disease, many barriers exist in the cascade of care for latent tuberculosis infection, including the need to certify healthcare professionals for reading tuberculin skin tests (TST). This paper proposes and evaluates a simple protocol for TST reading training. Primary care workers from different backgrounds received a 2-hour theoretical course, followed by a practical course on bleb reading. Blebs were obtained by injecting saline into sausages and then in volunteers. A certified trainer then evaluated the effectiveness of this protocol by analyzing the trainees' ability to read TST induration in clinical routine, blinded to each other's readings. Interobserver agreement was analyzed using the Bland-Altman test. The trainees' reading accuracy was calculated using two cut-off points - 5 and 10mm - and the effect of the number of readings was analyzed using a linear mixed model. Eleven healthcare workers read 53 saline blebs and 88 TST indurations, with high agreement for TST reading (0.07mm average bias). Sensitivity was 100% (94.6; 100.0) at 5mm cut-off and 87.3% (75.5; 94.7) at 10mm cut-off. The regression model found no effect of the number of readings [coefficient: -0.007 (-0.055; 0.040)]. A simple training protocol for reading TST with saline blebs simulations in sausages and volunteers was sufficient to achieve accurate TST induration readings, with no effect observed for the number of readings. Training with saline blebs injected into voluntary individuals is safer and easier than the traditional method.
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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 | 0.019 | 0.026 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.017 | 0.008 |
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".