Application of the Edmonton Obesity Staging System in a Lifestyle Medicine Clinic: A Cross-Sectional Analysis
Bibliographic record
Abstract
The worldwide prevalence of obesity has steadily been on the rise for the past several decades, and this necessitates strategies that go beyond the traditional body mass index (BMI)-based methods. The Edmonton Obesity Staging System (EOSS) offers a more calibrated and clinically useful approach to assessing the multitude of risks attributable to obesity. In this study, we use the EOSS in a primary care-based lifestyle medicine clinic to assess the profile of our patient population and discern which of our patients might be more at risk for obesity-related outcomes. This cross-sectional study was conducted on 960 patients who visited the lifestyle medicine clinic at Prince Sultan Military Medical City in 2023. Data were obtained from electronic medical records. The patient characteristics were summarized using descriptive and analytical statistics. The cohort primarily consisted of females (70.6%) with a mean age of 39.17 years. The most common comorbid conditions were prediabetes (70.6%) and dyslipidemia (61.8%). EOSS stage classification found 23.6% to be stage 0, 58.8% stage 1, and 17.6% stage 2; no subjects were stage 3–4. Male gender and age of 40 years and above were significantly had higher EOSS stages, p < 0.001 for each. Moreover, anthropometric measurements, blood pressure, and HbA1c were significantly higher in patients with high EOSS stage. These results can assist in promoting the incorporation of EOSS into routine care to improve obesity care and patient outcomes in primary care.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| 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 | 0.000 | 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".