Clinical pharmacist perspectives for optimizing pharmacotherapy within Enhanced Recovery After Surgery (ERAS®) programs
Bibliographic record
Abstract
One of the most durable approaches to perioperative enhanced recovery programming has culminated in the formation of perioperative organizations devoted to improvements in the quality of the surgical patient experience, such as the Enhanced Recovery After Surgery (ERAS®) Society. Members of the American College of Clinical Pharmacy (ACCP) Perioperative Care Practice and Research Network (PRN) and officials from the ERAS® Society present an opinion that: (1) identifies therapeutic options within each pharmacotherapy-intensive area of ERAS®; (2) generates applied research questions that would allow for comparative analyses of pharmacotherapy options within ERAS® programs; (3) proposes collaborative practice opportunities between key stakeholders in the surgical journey and clinical pharmacists to manage drug therapy problems and research questions; and (4) highlights examples of pharmacist-led cost savings attributed to ERAS® implementation. Clinical pharmacists, working in this manner with the perioperative team across the care continuum, have optimized pharmacotherapy towards measurable outcomes improvements, and stand ready to partner with inter-professional stakeholders and organizations to advance the care of our mutual patients.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".