Bibliographic record
Abstract
See related article in EMBO Molecular Medicine http://dx.doi.org/10.1002/emmm.201201689 ‘The Great White Plague, which only 10 years ago was thought to be immune to drug therapy, is gradually being eliminated. . . Streptomycin pointed a way. Later supplemented with PAS (para‐amino salicylic acid) and more recently with isoniazid, it has brought the control of this disease within sight’. Selman A. Waksman, Noble Prize Banquet Speech 1952. Despite the breakthroughs in management of tuberculosis noted by Waksman 8 years after his discovery of the first effective antitubercular drug, streptomycin (Schatz et al, 1944), the disease continues to infect, incapacitate and kill in the 21st century. Over 8.8 million are infected and 1.4 million die every year (www.who.int/mediacentre/factsheets/fs104/en/index.html). The organism that causes tuberculosis, Mycobacterium tuberculosis , has proven to be a formidable match for our ability to control disease. At the core of this challenge is the remarkable physiology and biochemistry of M. tuberculosis and its ability to reside in a dormant state within human macrophages. These unique characteristics necessitate multidrug therapy, typically ≥4 antibiotics, and extended periods of dosing (6–12 months). The rise of strains resistant to multiple drugs MDR (multi‐drug‐resistant) resistant to rifampin and isoniazid, XDR (extensive‐drug‐resistant) resistant to even more antibiotics, and now some strains totally impervious to all available drugs, TDR (totally drug‐resistant) raises the specter of the emergence of a global health care disaster and a return to a pre‐Waksman era of tuberculosis treatment (Zumla et al, 2012). The result is a great need for new therapies and a renewed effort to find new anti‐TB antibiotics. The result is a great need for new therapies …
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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.007 | 0.014 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.007 | 0.013 |
| Insufficient payload (model declined to judge) | 0.030 | 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".