The utility of a Geographic Information System in the detection of Tuberculosis transmission
Bibliographic record
Abstract
Rationale: Traditional tuberculosis contact investigation focuses primarily on contacts named by the active case, most frequently family and friends.Geographic information systems (GIS) could be a novel tool for the identification of shared locations where there may be ongoing transmission. Methods:We conducted a retrospective review of public health charts for all 1536 active TB cases in Montreal from January 1996 to December 2004 inclusively.Patients' addresses for residential, work, school and social locations were abstracted and geo-coded.We then used Crimestat and R software to identify case points within a 15 meter radius of one another.M. tuberculosis isolates underwent IS6110-based genotyping, which we used as the gold standard to determine transmission.Results: Isolates from 1043 cases were successfully fingerprinted and compared.165 (15.8%) were assigned to 57 different clusters ranging in size from 2 to 11 members, based on identical numbers and molecular weights of IS6110 bands; the remaining 878 had unique genotypes.1284 locations were successfully geo-coded, including 196 of those with genotypic matches.Of these 196 locations with genetic matches, only 17 (8.7%)TB cases shared locations within 15 meters with other members of the same RFLP-defined cluster.However, using bootstrapping techniques to compare samples of equal size, of the 1088 locations for unique cases, a mean of 18.34 cases (95% CI: 7.94 -28.64) were within 15 meters of at least one other case.Conversely, among 348 instances of shared locations, 17 (4.9%)revealed matching genotypes. Conclusion:In a retrospective analysis where most locations referred to home addresses of persons with active TB, there was limited overlap between shared locations and shared mycobacteria.These analyses show that the conventional data collected by public health officials does not identify all locations or explain all instances of local transmission, thus limiting their ability to intervene with prevention and control.
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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 | 0.011 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".