P5.041 TB Treatment For HIV Positive Pregnant Women: Challenges to Screening and Diagnosis
Bibliographic record
Abstract
Background According to Kenya’s PMTCT guidelines, all HIV positive women who present for antenatal care should be tested for TB. Methods HIV-positive, pregnant women were recruited from two maternity hospitals in Nairobi, Kenya. The results presented here are based on surveys completed at baseline as well as 48 hour follow up. This data was collected from 505 women as part of a study on the use of mobile technology in PMTCT programmes. Questionnaires included questions on socio-economic characteristics, history of current and previous pregnancies, knowledge of PMTCT, TB screening and treatment and the use of Nevirapine. Chi-square tests and multivariable logistic regression were used to assess statistically significant associations between variables of interest and TB screening. Results Overall screening for TB in our sample was 10.3% with no significant difference between the two hospitals (11.4% versus 8.4%). Analysis also revealed no significant difference between groups based on sociodemographic status (including age, education, marital status and income) or based on the number of antenatal visits or gestational age at first presentation. Conclusion Reportedly, 80% of TB patients are given access to HIV testing and a further 27% of those who test pest positive are placed on ART. TB screening for pregnant women seem to be offered less regularly, however, with only 10% of women screened. In our sample, the lack of significant difference in screening between facility, by sociodemographic characteristics or by when they access services seems to suggest suboptimal TB screening in pregnant women is a systemic issue.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.099 | 0.006 |
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".