High yield of culture-based diagnosis in a TB-endemic setting
Bibliographic record
Abstract
BACKGROUND: In most of the world, microbiologic diagnosis of tuberculosis (TB) is limited to microscopy. Recent guidelines recommend culture-based diagnosis where feasible. METHODS: In order to evaluate the relative and absolute incremental diagnostic yield of culture-based diagnosis in a high-incidence community in Cape Town, South Africa, subjects evaluated for suspected TB had their samples processed for microscopy and culture over a 21 month period. RESULTS: For 2537 suspect episodes with 2 smears and 2 cultures done, 20.0% (508) had at least one positive smear and 29.9% (760) had at least one positive culture. One culture yielded 1.8 times more cases as 1 smear (relative yield), or an increase of 12.0% (absolute yield). Based on the latter value, the number of cultures needed to diagnose (NND) one extra case of TB was 8, compared to 19 if second specimens were submitted for microscopy. CONCLUSION: In a high-burden setting, the introduction of culture can markedly increase TB diagnosis over microscopy. The concept of number needed to diagnose can help in comparing incremental yield of diagnosis methods. Although new promising diagnostic molecular methods are being implemented, TB culture is still the gold standard.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 | 0.038 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".