Multimodal Prehabilitation for Gynecologic Cancer Surgery
Bibliographic record
Abstract
Surgical treatment is commonly employed to treat patients with gynecologic cancer, although surgery itself may function as a stressor, reducing the patients' functional capacity and recovery. Prehabilitation programs attempt to improve patients' overall health and baseline function prior to surgery, thereby enhancing recovery and lowering morbidity. In recent years, prehabilitation has come to primarily refer to multimodal programs that combine physical activity, nutritional support, psychological well-being, and other medical interventions. However, the specific methods of implementing prehabilitation and measuring its effectiveness are heterogeneous. Moreover, high-level evidence regarding prehabilitation in gynecologic cancer surgery is limited. This review provides a summary of multimodal prehabilitation studies in gynecologic oncologic surgery. Enhanced postoperative recovery, lower postoperative complications, lower rate of blood transfusions, and faster gastrointestinal functional recovery have been reported after multimodal prehabilitation interventions. Patients and healthcare professionals should recognize the importance of prehabilitation in the field of gynecologic oncologic treatment, based on the emerging evidence. In addition, there is a need to establish an appropriate target group and construct a well-designed and tailored prehabilitation program.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".