Tuberculosis cases missed in primary health care facilities: should we redefine case finding?
Bibliographic record
Abstract
SETTING: This study was conducted in Cape Town in two primary health care facilities in a sub-district with a high prevalence of bacteriologically confirmed pulmonary tuberculosis (TB). OBJECTIVE: To determine the proportion of adults with respiratory symptoms who attend health care facilities but are not examined for nor diagnosed with TB in facilities where routine TB diagnosis depends on passive case finding. DESIGN: A total of 423 adults with respiratory symptoms exiting primary health care services were consecutively enrolled during April-July 2011. RESULTS: Twenty-one (5%) participants were diagnosed with culture-positive TB. None had sought care at the facility for their respiratory symptoms, none were asked about respiratory symptoms during their visit and none were asked to produce a sputum sample. Nine cases had attended the facility for reasons regarding their own health, while 12 cases were accompanying someone else attending the facility, or for another reason. CONCLUSION: Patients with infectious TB attend primary health care facilities, but are not recognised and diagnosed as cases. Health care staff should search actively within facilities for cases who attend the health care services to ensure that cases are not missed. Intensified case finding should start within the facility, and should not be limited to patients who report respiratory symptoms or who are human immunodeficiency virus positive.
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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 | 0.017 | 0.116 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.006 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".