Is there a fundamental flaw in Canada’s post-arrival immigrant surveillance system for tuberculosis?
Bibliographic record
Abstract
BACKGROUND: New immigrants to Canada with a history of tuberculosis or evidence of old healed tuberculosis on chest radiograph are referred to public health authorities for medical surveillance. This ostensible public health protection measure identifies a subgroup of patients (referrals) who are at very low risk (compared to non-referrals) of transmission. METHODS: To assess whether earlier diagnosis or a different phenotypic expression of disease explains this difference, we systematically reconstructed the immigration and transmission histories from a well-defined cohort of recently-arrived referral and non-referral pulmonary tuberculosis cases in Canada. Incident case chest radiographs in all cases and sequential past radiographs in referrals were re-read by three experts. Change in disease severity from pre-immigration radiograph to incident radiograph was the primary, and transmission of tuberculosis, the secondary, outcome. RESULTS: There were 174 cohort cases; 61 (35.1%) referrals and 113 (64.9%) non-referrals. Compared to non-referrals, referrals were less likely to be symptomatic (26% vs. 80%), smear-positive (15% vs. 50%), or to have cavitation (0% vs. 35%) or extensive disease (15% vs. 59%) on chest radiograph. After adjustment for referral status, time between films, country-of-birth, age and co-morbidities, referrals were less likely to have substantial changes on chest radiograph; OR 0.058 (95% CI 0.018-0.199). All secondary cases and 82% of tuberculin skin test conversions occurred in contacts of non-referrals. CONCLUSIONS: Phenotypically different disease, and not earlier diagnosis, explains the difference in transmission risk between referrals and non-referrals. Screening, and treating high-risk non-referrals for latent tuberculosis is necessary to eliminate tuberculosis in Canada.
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 | 0.059 | 0.125 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.006 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| 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".