Fatores associados ao atraso no diagnóstico da tuberculose pulmonar no estado do Rio de Janeiro
Bibliographic record
Abstract
OBJECTIVE: To estimate the total time elapsed between symptom onset and diagnosis of pulmonary tuberculosis (patient delay plus health care system delay), analyzing the factors associated with delayed diagnosis in the state of Rio de Janeiro, Brazil. METHODS: We conducted a questionnaire-based survey involving 218 pulmonary tuberculosis patients treated for two months at 20 health care clinics and 3 hospitals in eight cities within the state of Rio de Janeiro. We collected socioeconomic and demographic data, as well as data regarding the health care system and the medical history of the patients. RESULTS: The median time elapsed from the onset of symptoms to diagnosis was 68 days (interquartile range [IQR]: 35-119 days). The median patient delay (time from symptom onset to initial medical visit) was 30 days (IQR: 15-60 days), and the median health care system delay (time from initial medical visit to diagnosis) was 21 days (IQR: 8-47 days). A cut-off point of 21 days was adopted. The factors independently associated with patient delay were female gender, cough, and unemployment [adjusted OR (95% CI) = 2.7 (1.3-5.6); 11.6 (2.3-58.8); and 2.0 (1.0-3.8), respectively], whereas only female gender was independently associated with health care system delay (OR= 3.2; 95% CI: 1.7-6.0). CONCLUSIONS: Delayed diagnosis of pulmonary tuberculosis remains a problem in Rio de Janeiro, increasing the risk of transmission and mortality, that risk being greater for women and the socioeconomically disadvantaged. Patients might not recognize the significance of chronic cough as a health problem. Tuberculosis education programs targeting women might improve this situation.
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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 | 0.005 |
| 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) | 0.001 | 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".