Outcome of Tuberculosis Treatment: A Comparison between Alberta and Nicaragua
Bibliographic record
Abstract
OBJECTIVE: To measure the outcome of tuberculosis treatment in a low incidence, high income region, Alberta, and compare with an intermediate incidence, low income country with a model national tuberculosis program, Nicaragua. DESIGN: All 1992 sputum smear-positive pulmonary cases from both regions were included. Treatment outcome was assigned retrospectively to Alberta cases according to the International Union Against Tuberculosis and Lung Diseases' (IUATLD) criteria of cure, failure, transfer, absconder and death. SETTING: Alberta laboratories are required to report all Mycobacterium tuberculosis cultures to Alberta provincial tuberculosis services. Nicaragua cases are reported centrally to the Programa de control de tuberculosis in Managua using the IUATLD criteria. MAIN RESULTS: In Alberta, 222 tuberculosis cases were identified, of which 61 were smear positive. Nicaragua had 1552 smear positive cases of 2885 tuberculosis cases. Alberta's outcomes were 82% cured, no failed treatment, 5% absconded, 2% transferred and 11% died; Nicaragua's outcomes were 77% cured, 2% failed, 13% absconded, 5% transferred and 4% died. There was no significant difference in cure rates between Alberta and Nicaragua, P=0.33. CONCLUSIONS: Treatment outcomes can be measured effectively and reported in high income, low incidence settings. Alberta is achieving comparable cure rates with the Nicaraguan national tuberculosis program.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".