Use of chest radiography in the 22 highest tuberculosis burden countries
Bibliographic record
Abstract
An estimated 9 million new tuberculosis (TB) cases and 1.5 million deaths were caused by Mycobacterium tuberculosis in 2013 [1], more than 80% of which occurred in the 22 highest TB burden countries (HBCs). Among the confirmed incident cases, 4.9 million were pulmonary TB (PTB), of which 58% were bacteriologically confirmed. For many of these cases, chest radiography (CXR) was used as an important tool for triaging, particularly in smear-negative patients, to select patients for further microbiological workup with culture or Xpert MTB/RIF (Cepheid, Sunnyvale, CA, USA) [2, 3]. For the diagnosis of 42% of PTB cases who were microbiologically negative, CXR was often used to support the clinical decision, particularly in children [1, 4]. CXR is used widely in the 22 highest TB burden countries but we need strategies for cost and human resources We thank all participants of the 22 high TB burden countries for their time and support. We additionally would like to thank Yogesh Jha (Médecins sans Frontières, Paris, France) and Faiz Ahmad Khan (Montreal Chest Institute, Montreal, QC, Canada) for contacting additional participants in countries where it was difficult to receive a response. Srinath Satyanarayana (McGill University, Montreal, QC, Canada), Neeraj Raizada (Foundation for Innovative Diagnostics, Geneva, Switzerland), Sandra Kik (KNCV Tuberculosis Foundation, The Hague, The Netherlands) and Sarder Hossain (TB control program, BRAC, Dhaka, Bangladesh) helped greatly in improving the survey instrument.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.003 |
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".