Evaluation of the Effectiveness of Deep Learning Model in Detection and Classification of Pressure Injury
Bibliographic record
Abstract
Objective: The study was conducted to determine the effect of the deep learning model on the knowledge and satisfaction levels of nurses in the detection and classification of pressure injuries. Method: The population of this randomized controlled trial consisted of nurses working in intensive care, internal medicine, and surgical clinics at a foundation university hospital between March and April 2022 who voluntarily participated in the study. The sample consisted of a total of 60 (30 experimental and 30 control) nurses who met the sample criteria. The research data were collected using the Structured Nurse Introduction Form, Modified Pieper Pressure Injury Knowledge Test and Nurse Satisfaction Scale.The research data were analyzed in the SPSS 25.0 program. Results: The mean age of the nurses in the experimental group was determined as 25.67±7.27, and the control group as 25.10±3.47. 50% of the nurses in the experimental and control groups graduated from health vocational high schools, and 40% of them worked in surgical services. When the nurses' post-training knowledge exam (post-test) scores were compared; the mean score of the experimental group was determined as 39.36±1.88 and the control group as 33.30±1.68. The post-training knowledge level of the experimental group was found to be statistically significantly higher than the control group (P
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".