Enhanced recovery after surgery (ERAS) guided gynecologic/oncology surgery – The patient’s perspective
Bibliographic record
Abstract
• Awareness of ERAS was low in participants undergoing ERAS-guided gynecologic/oncology surgery. • Participant expectations were shaped by previous health experiences and information provided by healthcare providers. • Positive experiences of ERAS-guided surgery were reported when expectations aligned with delivery of care. • Patient education comprises a key element of the ERAS pathway, thus there is a need for significant improvement. • Providing personalised information can empower patients to actively engage in their care, which is required. Enhanced recovery after surgery (ERAS) pathways have demonstrated improvements in outcomes following benign gynecologic and gynecologic oncology surgery. However, there is limited data reporting the benefit of ERAS from the patient’s perspective. This study aimed to explore patient knowledge of and experience with ERAS-guided surgery. This interpretive descriptive study included participants who had undergone ERAS-guided gynecologic and gynecologic oncology surgery in Alberta, Canada using convenience sampling. Semi-structured interviews explored patient knowledge of ERAS, overall experience with surgery and recommended changes for surgical care. An inductive thematic analysis was conducted. Eight females aged 26–76 years old participated in the study who had gynecologic (n = 4) and gynecologic oncology (n = 4) surgery. Six themes central to participant experience of ERAS-guided surgery were identified: patient expectations, individual motivation, values and support, healthcare provider communication, trust in healthcare providers, COVID-19 and care co-ordination. Overall, specific knowledge of ERAS was low. Expectations were set by previous experience of healthcare (previous surgery or occupation), as well as information provided by healthcare professionals. Participants whose expectations aligned with physical experience of ERAS provided favourable perspectives. Participants recommended improving the quality, relevance and availability of information and establishing accessible follow up strategies. Based on the finding that knowledge about ERAS was minimal, we advocate for improved education pertaining to ERAS recommendations. Acknowledging patients’ expertise and motivation to engage in their care maybe one strategy to improve compliance with ERAS guidelines and improve outcomes for both patients and the healthcare system.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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 teacher head, 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".