Potential of use of modern information technology solutionsin the work of hospital infection control team, includingantibiotic therapy optimisation, reduction of alert pathogeninfections and vaccination popularisation
Bibliographic record
Abstract
F -literature Search, G -Funds CollectionBackground.Classic hospital information technology (it) systems are being upgraded to new functional levels due to the rapid evolution and introduction into everyday operation emerging solutions based on machine learning (Ml), artificial intelligence (ai), augmented reality (aR) and large language models (llM), including the famous ChatGPt.Objectives. the objective of the study was identification of potential practical applications of emerging it solutions to various aspects of identified routine activities of iCts, including hospital hygiene improvement, antibiotic stewardship adherence and vaccination enforcement and popularisation.Material and methods.Related merit and legally defined duties of infection control teams (iCts) at Polish hospitals were examined and compared against the capabilities of emerging it solutions.Results. it presents as inevitable that personal universal ai-based virtual assistant of medical staff, melting together modern broadband wireless connection to expanding it infrastructure and its new emerging solutions, including Ml, llM and already relatively inexpensive aR tools, will revolutionise the everyday practice of hospital epidemiology.Conclusions.it is foreseeable that in the near future, all hospital it tools will present a single coherent solution oriented on the common goal of achieving maximal patient health and staff safety, providing holistic, individualised, continuous and omnipresent support to Hospital infection Control teams, every other member of the hospital staff, all hospitalised patients, as well as their accompanying persons.
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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 | 0.006 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".