Effect of designed nursing guidelines on acute anal fissure treatment outcomes
Bibliographic record
Abstract
Objective: Anal fissure is a common problem through the world, it causes considerable morbidity. The aim of the study was to evaluate the effect of designed nursing guidelines on acute anal fissure treatment outcomes.Methods: Research design: Quasi-experimental design. Setting: General Surgery Wards and Outpatient Clinics of General Surgery at Assiut University Hospital. Sample: A purposive sample of 60 male and female adult patients diagnosed of having acute anal fissure. Patients were equally divided on random basis into two equal groups (study and control) 30 patients for each. Tools: Tool I-Patient assessment sheet. Tool II-Numeric Pain Rating Scale. Tool III-Bates-Jensen Wound Assessment Tool.Results: No statistically significant difference was found between the study and the control groups as regard demographic data. There was a statistically significant improvement in the pain level and wound healing among the study group (1.63 ± 2.08 and 11.93 ± 4.5 respectively) than in the control group (2.87 ± 2.33 and 14.43 ± 4.29 respectively). Also, there was a high statistically significant improvement in the level of knowledge of the study group than their level before applying the guidelines (p < .001).Conclusions: Designed nursing guidelines had a statistically significant effect on improving patients' knowledge, pain level, and wound healing among the study group patients than among the control group ones with acute anal fissure. Recommendations: Patients teaching should be an integral part of the nurses' duty in all hospitals. Further studies on larger sample from different geographical areas in Egypt to generalize the results.
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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 | 0.003 | 0.012 |
| 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 | 0.001 | 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".