Prehabilitation: Are There Sex- and Gender-Specific Issues in Surgery Preparation?
Bibliographic record
Abstract
BACKGROUND: Prehabilitation programs have advanced considerably; however, critical issues related to sex- and gender-specific factors remain largely unaddressed. Historically, research has been male-centered due to the underrepresentation of females in clinical trials, often attributed to concerns over hormonal variability. This focus has resulted in significant knowledge gaps and potential biases that impact effectiveness across sexes. We aim to highlight the need for integrating sex- and gender-specific considerations into prehabilitation to optimize surgical outcomes and promote equitable care for all patients. SUMMARY: Both biological (sex-related) factors, such as differences in muscle mass, metabolism, and body composition, and social (gender-related) factors, such as caregiving roles and stress management, influence individuals' responses to presurgical preparation. A review of the existing literature reveals a scarcity of data on sex and gender differences in prehabilitation, highlighting a major barrier to designing equitable and effective programs. Evidence underscores that comprehensive prehabilitation approaches, integrating physical, nutritional, and psychological elements, must account for these differences to optimize recovery outcomes. KEY MESSAGES: Sex- and gender-specific factors significantly shape patients' responses to prehabilitation and should be systematically incorporated into program design. The current lack of research on these differences limits the effectiveness of prehabilitation strategies, emphasizing the need for focused investigations. Addressing these gaps will facilitate the development of tailored, equitable prehabilitation programs that enhance presurgical care and improve recovery outcomes for all patients.
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 | 0.006 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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 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".