The Edmonton Obesity Staging System: assessing a potential tool to improve the management of obesity surgery in the Brazilian public health services
Bibliographic record
Abstract
Background: Limited access to publicly funded, insurance-covered, and self-paid obesity surgery is a reality worldwide. Waiting lists for procedures are usually based on chronologic criteria and body mass index (BMI)-defined obesity categorization. Obesity classification systems assess overall health and have been proposed as an alternative. Objective: To investigate the correlation between BMI-based classification and the Edmonton Obesity Staging System (EOSS) to support current evidence that the assessment of the clinical severity of obesity could be a helpful tool to maximize access to surgery. Setting: University hospital, Brazil. Methods: Retrospective analysis of all 2011 to 2014 adult patients who underwent obesity surgery under the public health system. Data on sex, age, presurgical BMI, and co-morbidities were extracted from hospital records. Spearman correlation coefficients were used to assess the strength and direction of the relationship between BMI classification and EOSS. Results: Of 565 patients, 79% were female, mean age 44.1 +/- 10.9 years and mean BMI 46.9 +/- 6.2 kg/m(2). The most common EOSS stage was 2 (86.5%), followed by stages 3 (8.5%) and 1 (4.9%). There was no correlation between the severity of obesity measured by BMI and EOSS (p = - .030, P = .475). Older patients had higher Edmonton scores (p = .308, P < .001). No difference was observed regarding sex. Conclusions: No correlation was found between EOSS and BMI and between these and sex. Age correlated with both obesity indicators. EOSS was reproducible in Brazilian surgical patients and may be an important tool from a health services perspective contributing to the more efficient use of limited resources for obesity surgery.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".