Down-staging of obesity one year after bariatric surgery: a new proposal of Edmonton obesity staging system
Bibliographic record
Abstract
Background: Different approaches are used to classify obesity severity. The Edmonton Obesity Staging System (EOSS) considers medical, physical and psychological parameters. A new modified EOSS with a different functional evaluation method, measuring Cardiorespiratory Fitness (CRF), has been recently proposed, EOSS-CRF. Bariatric surgery (BS) is one of the most efficient treatments of obesity and all aspect of related disorders. No studies have yet applied EOSS-CRF after BS. Therefore, the aim of this study was to evaluate modifications in EOSS and EOSS-CRF before and after BS. Methods: This observational study finally enrolled 72 patients affected by obesity. A multi-disciplinary assessment in order to evaluate eligibility to surgical treatment has been performed, including anamnesis, physical evaluation, anthropometric data measurement, biochemical blood exams and cardiopulmonary exercise testing. One year after BS the same protocol was applied. Patients have been classified according to EOSS and EOSS-CRF before and one year after BS. Results: After BS, patients categorized in classes associated to severe obesity (EOSS ≥ 2 or EOSS-CRF ≥ 2) reduced significantly. Using EOSS, patients without functional impairment were 61% before surgery and 69% after BS (p=0.383). Using EOSS-CRF, patients considered without functional impairment were only 9.7% before BS; this percentage significantly raised to 50% after BS (p<0.001). The impact of functional domains before and after BS is different in grading patients in EOSS and EOSS-CRF, respectively. Conclusions: Improvements obtained after BS are adequately summarized by EOSS and EOSS-CRF. The EOSS-CRF grading method for functional impairment seems to better reflect the known amelioration obtained after BS. Objective measurements of CRF may provide additional value to classify severity of obesity, also in the follow-up after BS.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".