Bibliographic record
Abstract
Infections with organisms that are resistant to various anti-microbial agents pose a serious challenge to effective management of infections. Resistance to antimicrobial agents, which may be intrinsic or acquired, has been noted in a wide variety of microorganisms causing human infections. These include resistance to antiviral agents in HIV, HBV, CMV and influenza virus, anti-parasitic agents in Plasmodium falciparum, anti-fungal agents in certain Candida species and MDR (multidrug-resistant) tuberculosis. It is however, the problem of multidrug-resistant bacterial infections (caused by MRSA, VRE, ESBL/AmpC/metallo-β lactamase producers and colistin-resistant Gram-negative bacilli) that has become a cause of major concern in clinical settings. Infections with these organisms can increase morbidity, mortality, increase the cost of therapy and increase the duration of hospitalization. The objective of this article is to review the question how early diagnosis of these infections, affects the overall management of infected or colonized patients, with regard to antimicrobial therapy.
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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".