Guidelines for Perioperative Care in Elective Abdominal and Pelvic Surgery at Primary and Secondary Hospitals in Low–Middle‐Income Countries (LMIC's): Enhanced Recovery After Surgery (ERAS) Society Recommendation
Bibliographic record
Abstract
Abstract Background This is the first Enhanced Recovery After Surgery (ERAS®) Society guideline for primary and secondary hospitals in low–middle‐income countries (LMIC's) for elective abdominal and gynecologic care. Methods The ERAS LMIC Guidelines group was established by the ERAS® Society in collaboration with different representatives of perioperative care from LMIC's. The group consisted of seven members from the ERAS® Society and eight members from LMIC's. An updated systematic literature search and evaluation of evidence from previous ERAS® guidelines was performed by the leading authors of the Colorectal (2018) and Gynecologic (2019) surgery guidelines (Gustafsson et al in World J Surg 43:6592–695, Nelson et al in Int J Gynecol Cancer 29(4):651–668). Meta‐analyses randomized controlled trials (RCTs), prospective and retrospective cohort studies from both HIC's and LMIC's were considered for each perioperative item. The members in the LMIC group then applied the current evidence and adapted the recommendations for each intervention as well as identifying possible new items relevant to LMIC's. The Grading of Recommendations, Assessment, Development and Evaluation system (GRADE) methodology was used to determine the quality of the published evidence. The strength of the recommendations was based on importance of the problem, quality of evidence, balance between desirable and undesirable effects, acceptability to key stakeholders, cost of implementation and specifically the feasibility of implementing in LMIC's and determined through discussions and consensus. Results In addition to previously described ERAS® Society interventions, the following items were included, revised or discussed: the Surgical Safety Checklist (SSC), preoperative routine human immunodeficiency virus (HIV) testing in countries with a high prevalence of HIV/AIDS (CD4 and viral load for those patients that are HIV positive), delirium screening and prevention, COVID 19 screening, VTE prophylaxis, immuno‐nutrition, prehabilitation, minimally invasive surgery (MIS) and a standardized postoperative monitoring guideline. Conclusions These guidelines are seen as a starting point to address the urgent need to improve perioperative care and to effect data‐driven, evidence‐based care in LMIC's.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".