Use of HDFx, a Novel Immunomodulator, to Stop the Germs from Winning in Hospitals and on The Battlefields : The Dangers of Antibiotic Resistance
Bibliographic record
Abstract
Getting admitted to a civilian or battlefield hospital these days often poses considerable risks and dangers. The ever-growing number of emerging diseases worldwide makes treatment of patients difficult and sometimes impossible. Many antibiotics are no longer effective against the simplest infections, which result in further hospitalizations with increased costs to the patients and governments worldwide. Microorganisms of prime concern include methicillin-resistant Staphylococcus aureus, Clostridium difficile, multidrug and extensive drug-resistant Mycobacterium tuberculosis, Neisseria gonorrhoeae, and carbapenem- resistant Enterobacteriaceae, as well as bacteria that produce extensive spectram beta-lactamases, such as E. coli. An unusual decline in the discovery of new and effective antibiotics, these days, is only making matters worse.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".