HDFx: A Novel Immunomodulator and Potential Superbug Super - Warrior for Hospitalized Patients and Battlefield Casualties
Bibliographic record
Abstract
Over the past decade, a disturbing trend in antimicrobial resistance of both gram -negative and gram-positive pathogens and "superbugs" has seriously complicated the treatment of many immuno-compromised, hospitalized patients [1-8]. Too this major problem must be added the numerous hospitalizations and deaths from contaminated meats, poultry, vegetables, seafoods, and dairy products [9-11]. Government resources, worldwide, are being overstretched and often remain powerless to combat these assaults on our populations. Almost one million people per year are killed by bacteria and "superbugs" due to antimicrobial resistance. If we add the untold millions per year who are dying from drug-resistant tuberculosis in Africa and India , the number of deaths becomes staggering. By about 2075, the number of people dying from drug-resistant infections could reach in excess of 35 million per year. Each year, in the USA alone, more than 150 million prescriptions are written, 60% of which are for antibiotics. Of these, it has been estimated that 50 million of these costly prescriptions are probably unnecessary
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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 | 0.000 |
| 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) | 0.007 | 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".