Factors influencing sex differences in numbers of tuberculosis suspects at diagnostic centres in Pakistan
Bibliographic record
Abstract
SETTING: DOTS-reporting tuberculosis (TB) diagnostic centres across Pakistan. OBJECTIVES: To quantitatively investigate the influence of diagnostic centre characteristics on the number of female and male TB suspects registered at diagnostic centres. DESIGN: Ten districts were selected across the four provinces of Pakistan. Data were collected on male and female TB suspects in all diagnostic centres within each district. A structured questionnaire was used to collect data on characteristics of the diagnostic centres. Multiple linear regression analysis was conducted to evaluate the influence of each characteristic on sex differences in the numbers of suspects. RESULTS: Two diagnostic centre characteristics were associated with higher numbers of female than male TB suspects: catering to the local catchment area (P = 0.001) and being accessible on foot (P = 0.002). The following characteristics were associated with higher numbers of male than female TB suspects: being open after 2 pm (P = 0.041), having more than five doctors working at the centre (P = 0.019), and having more than 100 suspects registered per quarter (P = 0.008). CONCLUSIONS: Smaller, local diagnostic centres that are accessible on foot registered more female than male TB suspects. More centralised facilities located further from homes, larger facilities and those with evening opening hours registered more male than female suspects.
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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 | 0.000 | 0.003 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".