Different Nursing Care Methods for Prevention of Keratopathy Among Intensive Care Unit Patients
Bibliographic record
Abstract
BACKGROUND: Patients with reduced consciousness level suffer from eye protection disorder and Keratopathy. This study was conducted to compare effect of three eye care techniques in prevention of keratopathy in the patients hospitalized in intensive care unit of Kermanshah. METHODS: This clinical trial was conducted in 2013 with sample size of 96 persons in three random groups. Routine care included washing of eyes with normal saline and three eye care methods were conducted with poly ethylene cover, liposic ointment, and artificial tear drop randomly on one eye of each sample and a comparison was made with the opposite eye as the control. Eyes were controlled for 5 days in terms of keratopathy. Data collection instrument was keratopathy severity index. Data statistical analysis was performed with SPSS-16 software and chi-squared test, Fisher's exact test, ANOVA and Kruskal-Wallis one-way analysis of variance. FINDINGS: The use of poly ethylene cover (0.59±0.665) was significantly more effective in prevention of keratopathy than other methods (P=0.001). There was no statistically significant difference between two care interventions of liposic ointment and artificial tear drop (P=0.844) but the results indicated the more effective liposic ointment (1.13±0.751) than the artificial tear drop (1.59±0.875) in prevention of corneal abrasion (P>0.001). CONCLUSION: Results of the study suggest the use of poly ethylene cover as a non-aggressive and non-pharmaceutical nursing and therapeutic method for prevention of keratopathy in the patient hospitalized in intensive care unit.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".