Development of a reconditioning program for elderly abdominal surgery patients: the Elder-friendly Approaches to the Surgical Environment–BEdside reconditioning for Functional ImprovemenTs (EASE-BE FIT) pilot study
Bibliographic record
Abstract
Elderly individuals who are hospitalized due to emergency abdominal surgery spend over 80% of their recovery time in bed, resulting in early and rapid muscle loss. As these elderly individuals have a lower physiological reserve, the impact of muscle wasting on function may be profound. The objectives of this study are to (1) create an independently led post-surgical reconditioning program and (2) pilot its implementation, while assessing the feasibility and safety of the program. The BE FIT program was generated with hospital rehabilitation staff to target lower limb strength, balance, and endurance. This pilot study was assessed using a sequential before and after trial, with a cohort of patients aged ≥ 65 years enrolled in the Elder-friendly Approaches to the Surgical Environment (EASE) study. Change in 30-s sit-to-stand performance between postoperative day 2 and discharge was compared between Usual Care pre- and post-BE FIT participants. A total of 66 patients participated in the sub-study, 33 Usual Care and 33 BE FIT. Mean (SD) age was 76.2 (8.78); 44 (67%) were female, with 11 (17%) reporting mild/moderate frailty on the CHSA Clinical Frailty Scale. BE FIT participants had a median of three rehab days and self-reported completing an average of 83% of the exercises. The adjusted between group difference showed that the BE FIT patients were able to complete more stands than the Usual Care (1.9 stands (0.94), p = 0.05). There were no reported adverse events. The reconditioning program was shown to be safe and feasible within the hospital setting for the elderly emergency abdominal surgery patients. More rigorous assessment is needed to confirm this effectiveness and to better assess patient adherence to self-directed exercise. Registration # NCT02233153 through ClinicalTrials.gov. Registered September 8, 2014.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".