Preoperative functional assessment and optimization in surgical patient: changing the paradigm
Bibliographic record
Abstract
Functional capacity has been shown to be a major determinant of surgical outcome since it is related to postoperative complications, activity and daily function, level of independence and quality of life. Anesthesiologists as "perioperative physicians", can identify those scoring systems that assess functional capacity, whether from the basic physical history and walk test to the most complex such as cardiopulmonary exercise testing, and formulate intraoperative and postoperative interventions (rehabilitation) to minimize the impact of surgery on the recovery process. Nevertheless, the preoperative period can be used as an opportune time to increase functional reserve in anticipation of surgery, thus enabling the patient to better withstand the metabolic cost of surgical stress (prehabilitation). There is a compelling evidence that prehabilitation programs, including physical exercise, nutritional optimization and relaxation strategies, can enhance preoperative physiological reserve, however further studies are needed to identify the most appropriate protocols for those patients at risk, and assess the impact of such programs on clinically meaningful surgical outcomes.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".