Medical dental prophylaxis of endocarditis
Bibliographic record
Abstract
Antibiotics have long been the main reason for the increase in man's longevity. Since their discovery, man has tried to reduce the level of infection by treating with antibiotics. At the same time, prophylactic use has been suggested, although this is controversial. Their routine use is not recommended, and empirical treatments at non-therapeutic doses, and indiscriminately, should be avoided, because they may become dangerous and harmful, causing among other things, the prevalence of resistant microorganisms and the eventual potentiation of an increase in morbid states. Infectious endocarditis is a systemic pathology that can start with a bacteremia, which comes either from dental procedures or/and chronic processes that already existed. Its etiopathogeny consists of a combination of bacteremia and two other factors: Cardiac injury, which can be congenital or/and acquired, and a debilitated immunological system (patients who have transplanted organs, or those who have auto-immune diseases, such as pemphigus vulgaris, systemic lupus erythematosus). The main goal is to prevent or to fight against the transient bacteremia, reducing its intensity and duration, and also to kill the bacteria in at-risk patients. In this way, infectious endocarditis can be prevented; the dental surgeon plays an important role in the prevention of this condition, which joins medical and dental aspects. This can be done by antibiotic prophylaxis. The dentist needs to be acquainted with the medical protocols of the heart health societies.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.008 | 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".