Results of a population screening intervention for tuberculosis in a Nunavik village, Quebec, 2015–2016
Bibliographic record
Abstract
Background: A small village in Nunavik, Quebec experienced a tuberculosis (TB) outbreak in 2012-2013 and then a resurgence in 2015-2016.Cases were still occurring, despite the fact that contact tracing had already been conducted on one quarter of the population.A decision was taken to conduct large-scale screening of the population for TB.Objective: To describe the results of a population-based TB screening intervention designed to identify individuals with latent TB infection (LTBI) or active TB requiring treatment.Methodology: The history of TB infection (either active TB or LTBI, defined as a positive tuberculin skin test result of at least 5 mm induration) and treatment (considered adequate if at least 80% of prescribed doses were taken) were determined.Those who were two years of age and older and had not been included in contact tracing after June 1, 2015 were included for TB screening (n=1,026 eligible individuals).Screening included a nurse assessment, tuberculin skin test (TST) for those with previous negative TST or of unknown status and chest X-ray for the others.Results: Of the eligible individuals in the affected village, 1,004 (98%) participated in the screening.Of these, 30% had a history of previous TB infection.A TST screening was administered to 71% of the participants, 10% of whom had positive results.Assessments were performed on 425 participants and 385 underwent a chest X-ray.Fifty-two cases of previously diagnosed active TB and three cases of new active TB were documented.In addition, there were 247 individuals with LTBI who had been previously identified (191 were found to have had adequate LTBI treatment, 56 were found to have had inadequate LTBI treatment) and 69 were identified with de novo LTBI.In addition, 633 participants were found to have no TB infection.There were 125 participants who were referred for LTBI treatment.Follow-up information was available for 120 and 85 (71%) of these completed the treatment.Conclusion: Within this northern village, which had persistent TB transmission despite classic control measures, population-based screening had a high degree of coverage and was an effective way to detect additional cases of individuals with active TB and those with LTBI.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".