Implementation of an enhanced recovery program framework in medical oncology.
Bibliographic record
Abstract
66 Background: Enhanced recovery programs (ERPs) apply multi-modal approaches to manage symptoms, decrease complications, and reduce length of stay (LOS). Widely adopted in surgical settings, there is limited evidence of their implementation in non-surgical patient cohorts. An ERP was implemented in a medical oncology population at a National Cancer Institute-designated comprehensive cancer center. The aim of this quality improvement initiative was to evaluate the implementation of an enhanced recovery framework and determine feasibility in medical oncology. Methods: Enhanced recovery in medical oncology (ERMO) was implemented using Plan, Do, Study, Act methodology. Implementation included introduction of integrative medicine, opioid sparing alternatives, fluid and nutrition management, and functional mobility. Outcome measures included symptom distress as measured by the Edmonton Symptom Assessment Scale (ESAS), return to intended oncologic therapy (RIOT), LOS, and opioid use, evaluated from January 2017 through April 2018. Results: A total of 50 patients were evaluated during the program implementation, and compared with 49 control patients retrospectively reviewed prior to ERMO implementation. Average LOS for ERMO patients was 7.3 days compared to 5.5 days for the control group. Time to RIOT averaged 18.9 days for control patients (n = 30) versus 20.8 days for ERMO patients (n = 17). Nineteen patients (38%) had a reduction in morphine equivalent daily dose (MEDD) from admission to discharge, with an average MEDD of 328.47 milligrams per patient. Conclusions: ERMO as a framework is feasible. The patient reported outcomes such as ESAS and RIOT, and barriers to implementation, including participant engagement and patient pain management perceptions, should be evaluated in the context of larger clinical trials.
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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 | 0.012 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".