Exploring the Application of a Multi-Targeted Nursing Group for Enhanced Recovery After Surgery Using the LEER (“Less Pain”, “Early Movement”, “Early Return to a Normal Diet” and “Reassurance”) Model
Bibliographic record
Abstract
OBJECTIVE: To explore the effects and value of establishing a multi-target nursing group (MTNG) for facilitating goal-oriented enhanced recovery after surgery (ERAS) using the LEER ("less pain", "early movement", "early return to a normal diet" and "reassurance") model. METHODS: The clinical data of 198 patients with hepatobiliary and pancreatic malignancies were retrospectively analyzed. The patients were divided into two groups: 91 cases were collected in a traditional group, which adopted traditional perioperative care, and 107 cases were collected in an MTNG group, which adopted MTNG measures. The differences in the clinical data including postoperative recovery, unplanned readmission rate, the implementation rate of nursing measures, the degree of a patient's understanding of the disease, and patient compliance and satisfaction with nursing care during hospitalization were compared and analyzed between the two groups. RESULTS: The MTNG group reflected a lower pain degree and hospitalization expenses (P < 0.05), earlier postoperative flatulence, earlier recommencing of a normal diet, and earlier postoperative ambulation (P < 0.05), together with a shorter postoperative indwelling catheter duration and length of hospital stay (P < 0.05). There were no significant differences in the incidence of postoperative complications and unplanned postoperative readmission rates between the two groups (P > 0.05). The implementation rate of nursing measures and the degree of patients understanding the disease, and patient compliance and satisfaction with nursing care were higher in the MTNG group (P < 0.05). CONCLUSION: The MTNG approach, based on ERAS with the LEER model, was conducive to the safe and rapid postoperative recovery of patients, the precise and efficient implementation of ERAS measures, the improvement of medical treatment satisfaction among patients.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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) | 0.001 | 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".