Yield of casual contact investigation by the hour.
Bibliographic record
Abstract
OBJECTIVE: Among casual contacts of tuberculosis (TB) patients, to assess how duration of contact, prior mycobacterial exposure, and performance of one or two tuberculin skin tests (TST), affect the likelihood that a positive TST represents conversion. METHODS: Published estimates of mycobacterial prevalence and BCG coverage, and their effect on single or repeated TSTs, were used to calculate baseline prevalence of TST reactions in four populations commonly encountered in North American contact investigations. Using published estimates of hourly risk of TB infection, the probability that a positive TST represented conversion was calculated. RESULTS: Among casual contacts with 20 hours of exposure, the likelihood that a single positive TST performed after 8 weeks represented conversion was 77% in persons from populations with low prior mycobacterial exposure, but only 6-8% in foreign-born populations. If tuberculin testing was performed immediately and then again 8 weeks post-exposure, 14-38% of all positive tests would be due to boosting, related to prior exposure to mycobacteria or BCG. If one TST, performed 8 weeks after exposure, was positive in casual contacts from populations with high prevalence of prior mycobacterial exposures, the likelihood of true conversion was less than 40%, even after 200 hours of exposure. CONCLUSIONS: A single TST performed 8 weeks after the end of exposure among casual contacts will detect all true conversions, and minimize misdiagnosis due to boosting. The decision to perform TST on casual contacts should consider the likelihood of prior mycobacterial exposure in the population, as well as the duration of exposure.
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 | 0.002 | 0.016 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".