Costs of Tuberculosis at 3 Treatment Centers, Canada, 2010–2016
Bibliographic record
Abstract
A fter marked declines in tuberculosis (TB) inci- dence in Canada during the second half of the 20th century (1), progress toward elimination has stalled (2).Although a focus on detection and treatment of TB disease was highly effective in the past, changing epidemiology has limited the impact of this approach in reaching elimination.Additional approaches are needed.These approaches may include more targeted efforts for disproportionately affected populations, such as some Indigenous communities (2,3) and persons born outside of Canada ( 4).Yet health resources are scarce (5).A fundamental aspect of decision-making in health is understanding the trade-offs associated with potential interventions or programs in comparison to other interventions and programs within the broader health agenda.To achieve the greatest return (improved health) on investment (money spent), policymakers should have accurate cost estimates for the various elements of TB prevention and care.However, costs associated with TB in Canada have not been estimated since 2004 (6).With new tests and treatments available for TB infection and disease, updated cost estimates will support informed decision-making for resource allocation around existing and emerging interventions and programs (7-13).We sought to estimate the TB-related health system costs associated with managing persons treated for TB infection and different forms of TB disease, and the predictors of these costs, at 3 major TB treatment centers in British Columbia, Ontario, and Quebec, Canada. Methods Study Design and Participating TB Treatment CentersWe conducted a retrospective chart review of persons initiating treatment for TB infection, drug-susceptible Costs of Tuberculosis at 3 Treatment Centers, Canada, 2010-2016
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".