Comparative study of the incidences of hospitalinfections in the burn department: the years2015 vs. 2022
Bibliographic record
Abstract
AMA Nawara W, Śpiewak B, Gniadek A. Comparative study of the incidences of hospital infections in the burn department: the years 2015 vs. 2022. Nursing Problems / Problemy Pielęgniarstwa. 2023;31(1):21-28. doi:10.5114/ppiel.2023.129135. APA Nawara, W., Śpiewak, B., & Gniadek, A. (2023). Comparative study of the incidences of hospital infections in the burn department: the years 2015 vs. 2022. Nursing Problems / Problemy Pielęgniarstwa, 31(1), 21-28. https://doi.org/10.5114/ppiel.2023.129135 Chicago Nawara, Weronika, Beata Śpiewak, and Agnieszka Gniadek. 2023. "Comparative study of the incidences of hospital infections in the burn department: the years 2015 vs. 2022". Nursing Problems / Problemy Pielęgniarstwa 31 (1): 21-28. doi:10.5114/ppiel.2023.129135. Harvard Nawara, W., Śpiewak, B., and Gniadek, A. (2023). Comparative study of the incidences of hospital infections in the burn department: the years 2015 vs. 2022. Nursing Problems / Problemy Pielęgniarstwa, 31(1), pp.21-28. https://doi.org/10.5114/ppiel.2023.129135 MLA Nawara, Weronika et al. "Comparative study of the incidences of hospital infections in the burn department: the years 2015 vs. 2022." Nursing Problems / Problemy Pielęgniarstwa, vol. 31, no. 1, 2023, pp. 21-28. doi:10.5114/ppiel.2023.129135. Vancouver Nawara W, Śpiewak B, Gniadek A. Comparative study of the incidences of hospital infections in the burn department: the years 2015 vs. 2022. Nursing Problems / Problemy Pielęgniarstwa. 2023;31(1):21-28. doi:10.5114/ppiel.2023.129135.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".