Bibliographic record
Abstract
Lethal microorganisms have terrorized man since the beginning of time, killing more human beings than anything else in history. The most infamous epidemic, the Black Death, wiped out almost half the population of Europe. To quote H.G. Wells, "adapt or perish, now as ever, is nature's inexorable imperative." Superbugs are nature's revenge on humans for their ingenuity. For decades antibiotics, which work by honing in on particular bacteria, have been the chief line of defense against infection. There is growing urgency for the judicious assessment of both conventional and innovative strategies with regard to antibiotic use, infection control, molecular detection of pathogens and adequate treatment of multidrug-resistant organisms in hospitals, especially critical care units. Financial restraints, changing demographics, an aging population and the limited introduction of new antibiotics have established an imperative for utilization of goal directed strategies in infection prevention and control. Research and development of both clinical and environmental weapons to combat these adversaries is essential if man is to adapt, not perish, in this fight for survival. This article will provide a snapshot of advances in infection prevention and control, including evidence based design, as they relate to the critical care environment.
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 | 0.007 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.014 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.004 | 0.009 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".