Reducing the health impact of infectious agents: the significance of preventive strategies.
Bibliographic record
Abstract
Each year almost 15 million people die of infectious diseases and in all probability this figure is much higher, because in many cases infection is not at all recognized as being the cause of death or its contributory role is not known. There is an increase in the risks posed by infections; the belief in the omnipotence of drugs has not stood the test of time; rather, by adopting a counterstance we risk losing all that we accomplished during the last decades. After all, over the past 40 years we witnessed progress in the development of reliable and affordable anti-infectives and vaccines. As a result of this, today parents have their children vaccinated less often since they are no longer aware of the risks posed by lack of vaccination. This will give rise to the sudden reemergence of certain infectious diseases. And we overlook the fact that by observing basic rules of hygiene (hand hygiene; water decontamination; etc), we could save many lives on this earth. It is becoming increasingly more difficult to treat with antibiotics patients harboring resistant bacterial strains on their skin. Hospitals need surface disinfection to prevent microbes such as Clostridiium difficile or norovirus. The institutional use of alcohol-based hand disinfectants has by now become an accepted practice in North America. But some of these substances have dangerous side effects where humans and the environment are concerned. Our test methods are not always able to evaluate the actual extent of the risks posed.Prevention is accorded greater importance in view of the declining number of therapeutic measures available. But combating pathogenic microorganisms could, in turn, give rise to problems whose nature we cannot at all predict today. We need far greater knowledge of the pathogens and should be less naïve when embracing new technologies, which only seem to solve problems. What will be the long-term implications if the increasing selective pressure exerted on these bacteria induces them to become more resistant?We need an effective combination of treatment and vaccination strategies, together with a consistent prevention policy. Unlike drugs and vaccinations, disinfectants can be used in a consistent manner; they can simultaneously eliminate a vast range of pathogenic microorganisms, without having any major side effects.
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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 | 0.007 | 0.013 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.008 | 0.007 |
| Insufficient payload (model declined to judge) | 0.009 | 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".