Extent of pulmonary tuberculosis in patients diagnosed by active compared to passive case finding.
Bibliographic record
Abstract
OBJECTIVE: To compare the extent of pulmonary tuberculosis amongst patients detected by screening (active case finding) with that in patients detected by symptoms (passive case finding), and to identify early symptoms of pulmonary tuberculosis. SUBJECTS AND METHOD: In this cross-sectional study, Tuberculosis Control Program records were reviewed for method of detection and extent of disease in Canadian Plains Aborigines between 1 January 1991 and 30 June 1999. RESULTS: Among 903 cases, method of detection was active in 450 (49.8%) and passive in 453 (50.2%). Cough and fever were the most common symptoms in both methods of detection, and were significantly more frequent in passive detection (P < 0.05). Cough was present in 59% and fever in 19% of actively detected cases compared to 84% and 47%, respectively, of passively detected cases. Age was significantly different between the two methods of detection. Hemoptysis, weight loss and method of detection were associated with increased risk of infectiousness among those < or = 19 years, while cough, hemoptysis and weight loss were associated among those >19 years. CONCLUSION: Method of detection rather than age contributed to infectiousness in children and adolescents. Daily cough for more than 1 month and unexplained fever for more than 1 week should raise the suspicion for TB.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 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".