A population-based study of tuberculosis case fatality in Canada: do Aboriginal peoples fare less well?
Bibliographic record
Abstract
SETTING: The Province of Alberta, Canada. OBJECTIVES: To explore trends in tuberculosis (TB) case fatality, compare TB case-fatality rates by population group and determine prognostic factors associated with TB-related death in Alberta from 1996 to 2012. DESIGN: Retrospective cohort analysis. RESULTS: During the study years, all-cause TB case fatality fell from 10.7% to 6.3%; the fall was attributable to a change in population structure, as there were more foreign-born and fewer older cases with time. A stable 2% of TB cases died without treatment. Compared to other population groups, Canadian-born Aboriginal case patients were more likely to die without treatment and to die younger. Of TB deaths that were TB-related, 68.9% occurred before or during the initial phase of treatment; of these, TB was a contributory cause of death in 77.5%, i.e., another medical condition was the primary cause of death. In multivariate analysis, age >64 years, aboriginality and miliary/disseminated or central nervous system disease were independent predictors for TB-related death. CONCLUSION: Preventive therapy for those with latent tuberculous infection and a high-risk medical condition, early diagnosis of disease, and special support of older, Aboriginal or comorbid cases, once diagnosed, are necessary to further minimise TB case fatality in Alberta, Canada.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".