Sputum induction for tuberculosis diagnosis in an Arctic setting: a cost comparison
Bibliographic record
Abstract
SETTING: Tuberculosis (TB) incidence was 234 per 100 000 in Nunavut, Canada, in 2012. Until recently, some individuals seen in local clinics for presumed TB required costly air evacuation to Southern Canada (Ottawa) for investigation if they were unable to produce sputum spontaneously. OBJECTIVE: To estimate the cost per individual evaluated for TB, associated with the establishment of a sputum induction programme in Iqaluit, Nunavut, Canada. DESIGN: A decision analysis model compared the total cost per individual for two strategies: 1) initial investigation in Iqaluit, with transport to Ottawa for those requiring sputum induction; and 2) sputum induction at the hospital in Iqaluit, with further investigation in Ottawa only if needed. The model simulated diagnostic and treatment paths from the initial clinic visit to completion of TB investigation or treatment (when applicable). RESULTS: The estimated cost per person evaluated for TB with sputum induction in 1) Ottawa vs. 2) Iqaluit was CAD4798 (95% uncertainty range 2923-6650) vs. CAD2479 (1206-4256), respectively. Total costs were influenced by underlying TB prevalence, but local sputum induction consistently yielded cost savings. CONCLUSION: Providing sputum induction in a high-incidence Arctic community such as Iqaluit is projected to generate substantial cost savings in the investigation and management of individuals with presumed TB.
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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 | 0.006 | 0.014 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".