The correlation between the risk score and skin injuries in neonatal intensive care units
Bibliographic record
Abstract
Background.Preventive activities play an important role in today's healthcare systems.In this regard, the use of skin injury risk assessment tools in the neonatal intensive care unit (NICU) is advocated as an effective technique to decrease skin injury.Objectives.This study aimed to evaluate the relationship between risk score and skin injuries in newborns admitted to the NICU.Material and methods.This descriptive study was conducted on 265 newborns admitted to the NICUs in Tabriz, Iran.For data collection, we used the Skin Risk Assessment and Management Tool (SRAMT).Data was collected by repeated observations of newborns and was analysed using descriptive statistical methods and Spearman's correlation coefficient. Results.The mean risk score decreased from 19.85 on the first day of hospitalisation to 13.23 on the twenty-eighth day (scoring range from 8 to 32).During the study, 557 skin injury were reported, 84.91% of which occurred in the first week of hospitalisation.There was also a statistically significant correlation between risk score and skin injury (R = 0.37, p < 0.00). Conclusions.According to our results, a higher risk score was associated with an increased incidence of skin injuries.Thus, it is recommended that the risk score be developed through utilising risk prediction methods to identify newborns at risk of skin injuries.It is essential to develop skin care programmes and preventative measures in NICUs.
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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 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".