Technology and tuberculosis control: the OUT-TB Web experience
Bibliographic record
Abstract
OBJECTIVE: Develop a tool to disseminate integrated laboratory, clinical, and demographic case data necessary for improved contact tracing and outbreak detection of tuberculosis (TB). METHODS: In 2007, the Public Health Ontario Laboratories implemented a universal genotyping program to monitor the spread of TB strains within Ontario. Ontario Universal Typing of TB (OUT-TB) Web utilizes geographic information system (GIS) technology with a relational database platform, allowing TB control staff to visualize genotyping matches and microbiological data within the context of relevant epidemiological and demographic data. RESULTS: OUT-TB Web is currently available to the 8 health units responsible for >85% of Ontario's TB cases and is a valuable tool for TB case investigation. Users identified key features to implement for application enhancements, including an e-mail alert function, customizable heat maps for visualizing TB and drug-resistant cases, socioeconomic map layers, a dashboard providing TB surveillance metrics, and a feature for animating the geographic spread of strains over time. CONCLUSION: OUT-TB Web has proven to be an award-winning application and a useful tool. Developed and enhanced using regular user feedback, future versions will include additional data sources, enhanced map and line-list filter capabilities, and development of a mobile app.
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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".