Transforming the diagnosis of tuberculosis: an editorial board member’s opinion at the 15th year of<i>Expert Review of Molecular Diagnostics</i>
Bibliographic record
Abstract
Interview with Professor Madhukar Pai, MD, PhD by Claire Raison (Commissioning Editor). Professor Madhukar Pai did his medical training and community medicine residency in Vellore, India. He completed his PhD in epidemiology at the University of California, Berkeley (CA, USA) and a postdoctoral fellowship at the University of California, San Francisco (CA, USA). He is currently an associate professor of epidemiology at McGill University in Montreal (Canada). He serves as the Director of Global Health Programs, and as an Associate Director of the McGill International Tuberculosis Centre. In addition, he serves as a Consultant for the Bill & Melinda Gates Foundation. He also serves on the Scientific Advisory Committee of the Foundation for Innovative New Diagnostics, Geneva, Switzerland. His research is focused on improving the diagnosis and treatment of tuberculosis, especially in high-burden countries such as India and South Africa. His research is supported by grant funding from the Gates Foundation, Grand Challenges Canada and Canadian Institutes of Health Research. He has more than 200 peer-reviewed publications. He is recipient of the Union Scientific Prize, Chanchlani Global Health Research Award and Stars in Global Health award from Grand Challenges Canada, and is a member of the Royal Society of Canada.
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 | 0.021 | 0.072 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.017 | 0.020 |
| Insufficient payload (model declined to judge) | 0.004 | 0.004 |
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".