Tuberculosis surveillance among new immigrants in Montreal.
Bibliographic record
Abstract
SETTING: Foreign-born persons account for over 60% of Canadian tuberculosis (TB) incidence; immigrants with TB-related lung scarring ('inactive TB') are at particularly high risk, and represent an important target for preventive efforts. OBJECTIVE: To document the performance of the immigrant surveillance programme for inactive TB in Montreal. DESIGN: All immigrants arriving with inactive TB are referred by the public health department to the Montreal Chest Institute. We prospectively recorded clinical and radiographic data for those evaluated in 1999 and 2000. We examined physicians' adherence to Canadian guidelines. We also evaluated concordance of chest radiographic interpretation. RESULTS: Of 1444 immigrants notified, 792 (55%) were sent referral letters. Most of the others lacked valid addresses. Of the 654 (45%) who were examined, 322 (22%) were diagnosed with untreated latent TB, 215 (15%) were recommended therapy, and 156 (11%) completed it. Of 388 potential candidates for treatment of latent TB, 274 (71%) underwent tuberculin tests. Treatment decisions followed guidelines for 87% of patients with full testing. Agreement between clinicians and chest radiologists as to TB-related radiographic abnormalities was frequent (K 0.63). Six 'high volume' clinicians performed better than others with respect to management and radiographic interpretation. CONCLUSION: Centralised post-immigration surveillance requires more accurate referrals, and more consistent provider performance.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".