Healthcare Settings and Infection Prevention: Today’s Procedures in Light of the “Instructions for Disinfection” Issued During the 1817 Typhus Epidemic in the Grand Duchy of Tuscany (Pre-Unification Italy)
Bibliographic record
Abstract
Even today, healthcare-associated infections (HCAIs) remain the most frequent and serious complications in healthcare, with a significant clinical and economic impact. The authors of this manuscript address the causes and conditions that determine this situation and describe them in comparison with the situation in the Grand Duchy of Tuscany more than two centuries ago and with the instructions that were issued at the time to contain the typhus epidemic of 1817, increase hospital sanitation, and disinfect houses. Today, we know that a crucial element in the fight against healthcare-associated infections (HCAIs) is the definition and implementation of best care practices and other measures, according to a combined program that must be tailored to each healthcare setting. In the early nineteenth century, these approaches originated from experience and chemical knowledge that were becoming established, opening the way to the ideas and experiments of Ignác Fülöp Semmelweis and later of Joseph Lister, who traced the path for the birth of hygiene. Two centuries later the pioneering vision of the Grand Duchy of Tuscany at the beginning of the 19th century, when preventive measures in the field of public health were still backward and underdeveloped, is still enlightening and surprisingly topical.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".