Comparing the predictive ability of the Edmonton Obesity Staging System with the body mass index for use of health services and pharmacotherapies in Australian adults: A nationally representative cross‐sectional study
Bibliographic record
Abstract
Summary We assessed the value of the Edmonton Obesity Staging System (EOSS) compared with the body mass index (BMI) for determining associations with use of health services and pharmacotherapies in a nationally representative sample of participants in the 2011–2013 Australian Health Survey. A subsample of participants aged 18 years or over, with at least overweight (BMI ≥ 25 kg/m2) or central obesity (waist measurement of ≥102 cm for men; ≥88 cm for women), and who had provided physical measurements (n = 9730) were selected for analysis. For statistical significance of each predictor, we used logistic regression for model comparisons with the BMI and EOSS separately, and adjusted for covariates. For relative explanatory ability, we used the Nagelkerke pseudo R2, receiver operating characteristic curve, and area under curve statistic. The EOSS was significantly better than the BMI for predicting polypharmacy and most of the health service use variables. Conversely, the BMI was significantly better than the EOSS for predicting having discussed lifestyle changes relevant to weight loss with the primary care physician. Clinicians, health care professionals, consumers, and policy makers should consider the EOSS a more accurate predictor of polypharmacy and health service use than the BMI in adults with overweight or obesity.
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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 | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".