Quarterly Trends of TB Treatment Outcomes as Xpert MTB/Rif Rolled Out in Manicaland, Zimbabwe for 2017 and 2018
Bibliographic record
Abstract
Background: The World Health Organization target is to reduce to zero the number of death due to Mycobacterium tuberculosis by the year 2035. The earlier TB treatment is commenced, the less chance of a TB associated death and the higher the chance of successful treatment outcomes. Objective: The objective of the study was to evaluate quarterly trends of TB treatment outcomes as Xpert MTB/Rif testing services rolled out in Manicaland for 2017 and 2018.The study gave an opportunity for the Zimbabwe National TB Program to generate some evidence showing implementation of the intervention, Xpert MTB/Rif diagnostic test. Method: In the retrospective study , a total of 3277 TB patient variables were captured from facility TB registers .The population was all TB patients recorded in the 304 health facilities as having received TB treatment between 1 January 2017 and 31 December 2018. The scope of the study did not include capturing the full range of variables that are determinants of TB treatment outcomes. Results: The study demonstrates that the proportion of TB treatment outcome of died had a steady decrease from quarter one of 2017, which had 30/238 (12.6%) to quarter two 2018 which had 41/431 (9.5%). Then quarter three of 2018 the outcome died had a slight increase to just slightly above 10.0% , before going back to just below 10.0% in quarter four of 2018. The proportion of cured for 2017 first quarter was 90/238(37.8%) and that for 2018 quarter four was 147/313 (47.0%). TB treatment outcome of treatment completed for 2017 quarter one was 91/238(38.2%) then for 2018 quarter one, it was 205/464(44.1%).There was no noticeable trend in the two TB treatment outcomes of cured and treatment completed during the eight evaluated quarters. Conclusion: Overall the TB treatment outcome of died showed a decreasing trend over the eight quarters. In order to avoid inferring the decrease to Xpert MTB/Rif roll out, more variables that are determinants of TB treatment outcomes have to be analysed in future studies.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".