Evaluating The Effects of Fine Cooperation Program and Doctor-Nurse Integration on Patients with Chronic Wounds
Bibliographic record
Abstract
Objective: This study aimed to evaluate the effects of fine cooperation program of doctor-nurse integration on wound healing, patient???s psychological state and pain degree of patients with chronic wounds. Methods: A total of 100 patients with chronic wounds who were treated with conventional wound management from March 2016 to December 2018 were enrolled as a control group, while another 100 patients with chronic wounds who were treated with the fine cooperation program of doctor-nurse integration during January to October 2020 were selected as an observation group. Nursing satisfaction, general conditions (waiting time for dressing change, hospitalization time, and wound healing time) and wound healing grade were observed, and the pain degree (scored by the simplified McGill scale) and psychological state [evaluated by Self-rating Depression Scale (SDS) and Self-rating Anxiety Scale (SAS)] were compared between before intervention and on Day 5 of intervention. Results: The nursing satisfaction was higher and the waiting time for dressing change, hospitalization time and wound healing time were shorter in the observation group than those in the control group (P<0.05). On Day 5 of intervention, the present pain intensity, Visual Analogue Scale, pain rating index, SDS and SAS scores are seen declined in both groups compared with those before intervention, and they were lower in the observation group than those in the control group (P<0.05). The observation group had a higher wound healing grade than that of the control group (P<0.05). Conclusion: The fine cooperation program of doctor-nurse integration can shorten the waiting time for dressing change and hospitalization time.
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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) | 0.000 | 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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".