Is quarterly cohort analysis useful for assessing treatment outcomes in a low incidence country?
Bibliographic record
Abstract
SETTING: In the Czech Republic, a country regarded as having a low incidence of tuberculosis (TB), short-course chemotherapy (SCC) of TB has been implemented in routine practice country-wide. OBJECTIVE: To assess the outcome of SCC by quarterly cohort analysis of patients using the methodology recommended by the World Health Organization (WHO). DESIGN: Patients with newly detected bacteriologically confirmed pulmonary TB notified in 1998 were treated according to local recommendations (SCC) or with the WHO-recommended DOTS strategy. The type of chemotherapy and its outcome were evaluated 12 months later by quarterly cohort analysis. RESULTS: A total of 731 patients with bacteriologically confirmed pulmonary TB, 403 of them smear-positive, were assessed. The proportion of those treated under the DOTS strategy increased from 56.2% to 75.1%. Favourable treatment outcomes (cure or treatment completed) were achieved in 69.0% of patients in the first quarter and 74.0% in the fourth quarter. Only four treatment failures and 21 defaulters were recorded. A total of 129 patients (15-21% in different cohorts) died before or during treatment, mostly from causes not connected with TB. If this proportion were not taken into account, treatment efficacy would have attained 85%. CONCLUSIONS: Analysis of SCC based on quarterly cohorts proved feasible in routine conditions in a country with a low incidence of TB and ongoing TB control, and provided more information than once yearly analysis.
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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 | 0.020 | 0.045 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".