Methicillin-Resistant Staphylococcus aureus and Multidrug Resistant Tuberculosis: Part 2
Bibliographic record
Abstract
Drug resistant tuberculosis has been recognized since chemotherapy first became available. However, drug resistance has increased in many countries, and recently strains resistant to both rifampicin and isoniazid (multidrug resistant tuberculosis) have emerged. This review discusses the epidemiology of multidrug resistant tuberculosis (MDRTB), and the control of MDRTB in healthcare facilities. Relevant papers for this review were identified by a systematic literature search on Medline. MDRTB is already established world-wide, and although the overall problem of resistance remains low in the UK, it is of significant clinical importance due to its high case-fatality, higher transmission risk, and complex treatment. The key elements of MDRTB control are prompt recognition, confirmation and treatment of cases, and the institution of strict infection control procedures to reduce the airborne spread of infection from infectious patients to others. This review emphasizes the importance of a multidisciplinary approach to management, with liaison between tuberculosis physicians, the microbiology department, infection control team, consultant in communicable disease, and occupational health.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".