EDITORIAL Tuberculosis and HIV co-infection: do we have a
Bibliographic record
Abstract
s there any reason why a clinician should be interested in knowing about the state of the art in tuberculosis (TB) and HIV co-infection surveillance in Europe? A question like, ‘‘Is TB–HIV co-infection a relevant issue?’ ’ is probably easier to answer, at least at a global level: TB–HIV co-infection is responsible for almost 400,000 deaths every year and TB is by far the major killer of HIV-infected persons, being responsible for over a quarter of the global burden of HIV-associated deaths; people living with HIV/AIDS infected with Mycobacterium tuberculosis are at 20–30 times greater risk of developing TB compared with HIV-uninfected persons [1]. And in Europe? To control TB and HIV co-infection, which, together with multidrug-resistant TB, is today the hardest obstacle to overcome in curbing the TB epidemic, the World Health Organization (WHO) promotes a specific package, included in the
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 | 0.005 | 0.027 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.004 | 0.001 |
| Research integrity | 0.013 | 0.019 |
| Insufficient payload (model declined to judge) | 0.013 | 0.010 |
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".