Mind the gap: Time to address implementation gaps in tuberculosis diagnosis and treatment
Bibliographic record
Abstract
Tuberculosis (TB) was first identified by Robert Koch in 1882. Sadly, even after a century since Koch’s breakthrough discovery, TB continues to kill over 1.5 million people every year [1] . Every year, nearly 9.5 million new cases of TB occur worldwide [1] . Of these, nearly 3 million TB patients are considered ‘missing’—they are ei- ther not diagnosed, or not reported to TB control programs [1] . In 2014, according to the World Health Organization (WHO), about 80% of reported TB cases occurred in 22 countries [1] . The six countries that stand out as having the largest number of inci- dent cases in 2014 were India, Indonesia, Nigeria, Pakistan, People’s Republic of China, and South Africa [...]
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 | 0.033 | 0.108 |
| Meta-epidemiology (narrow) | 0.005 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.018 | 0.014 |
| Open science | 0.006 | 0.005 |
| Research integrity | 0.046 | 0.055 |
| Insufficient payload (model declined to judge) | 0.014 | 0.009 |
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".