The Potential Role of Objective Activity Monitoring in Off-Site Follow-Ups Post-Bariatric Surgery
Bibliographic record
Abstract
Title: The Potential Role of Objective Activity Monitoring in Off-Site Follow-Ups Post-Bariatric Surgery. Background: Weight gain and attendance at follow-up visits after bariatric surgery are of great concern for the multidisciplinary care team. Geography and schedules make attending follow-up visits increasingly difficult as time after surgery goes on. Recently, inexpensive commercially available activity monitors have become more common place, making information concerning physical activity and sedentary behaviours deliverable online, allowing for important patient lifestyle information to be transmitted to the multidisciplinary care team. The purpose of this study was to determine if off-site objectively monitored physical activity and sedentary time can describe health measures such as total body fat, abdominal adipose tissue (AAT), and weight maintenance long-term post-bariatric surgery. Methods and findings: 59 individuals who had undergone bariatric surgery wore an ActivPAL for seven consecutive days, monitoring physical activity and sedentary time and underwent one DXA scan to determine body composition. Linear regression shows that (moderate-to-vigorous physical activity (MVPA) explained 18.8% of variance in body fat (p=0.019) and 11.3% of the variance in AAT (p=0.033). Conclusions: Objective monitoring could offer beneficial information concerning patients’ health at post-surgical follow-up visits.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.003 | 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".