Potential mechanisms of multimodal prehabilitation effects on surgical complications: a narrative review
Bibliographic record
Abstract
Continuous advances in prehabilitation research over the past several decades have clarified its role in improving preoperative risk factors, yet the evidence demonstrating reduced surgical complications remains uncertain. Describing the potential mechanisms underlying prehabilitation and surgical complications represents an important opportunity to establish biological plausibility, develop targeted therapies, generate hypotheses for future research, and contribute to the rationale for implementation into the standard of care. In this narrative review, we discuss and synthesize the current evidence base for the biological plausibility of multimodal prehabilitation to reduce surgical complications. The goal of this review is to improve prehabilitation interventions and measurement by outlining biologically plausible mechanisms of benefit and generating hypotheses for future research. This is accomplished by synthesizing the available evidence for the mechanistic benefit of exercise, nutrition, and psychological interventions for reducing the incidence and severity of surgical complications reported by the American College of Surgeons National Surgical Quality Improvement Program (ACS-NSQIP). This review was conducted and reported in accordance with a quality assessment scale for narrative reviews. Findings indicate that prehabilitation has biological plausibility to reduce all complications outlined by NSQIP. Mechanisms for prehabilitation to reduce surgical complications include anti-inflammation, enhanced innate immunity, and attenuation of sympathovagal imbalance. Mechanisms vary depending on the intervention protocol and baseline characteristics of the sample. This review highlights the need for more research in this space while proposing potential mechanisms to be included in future investigations.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".