Understanding Obesity Management in CKD Patients
Bibliographic record
Abstract
Background: Obesity is a global epidemic that is directly and indirectly linked to progression of chronic kidney disease (CKD). Nephrologists' attitude towards obesity management is not understood. Methods: We surveyed 14 nephrologists practicing in an academic centre in London, Ontario, Canada to investigate their perception and management of obesity. Then we performed a retrospective chart review of patients in a CKD clinic with obesity (BMI >30kg/m2). Ten follow-up visits were randomly selected for each nephrologist between Jan-Dec 2019. Each chart was assessed for documentation of obesity and a management plan such as lifestyle counselling, pharmacologic intervention, or specialist referral. Results: There were 13 responses (93%). Responses from a 5-point Likert scale, agree and strongly agree, have been combined. All nephrologists agreed that obesity negatively impacts CKD patients. 92% reported that discussing obesity evokes a negative response and 39% thought patients want to discuss obesity. Interestingly, 0% of nephrologists thought patients know that obesity has effective treatments. 85% of nephrologists talked to their patients about obesity, but 0% felt that they had time to treat it. With regards to management, 54% of nephrologists were comfortable with non-pharmacologic treatment, but only one was comfortable with pharmacologic treatments. 85% of respondents felt that patients should be referred to a specialist. A total of 140 charts were reviewed with a mean age 66 years, weight 105 kg, and BMI 37 kg/m2. Only one chart had obesity as a clinical issue and documented a weight loss discussion using non-pharmacologic strategies. Conclusions: Our results suggest that obesity is rarely managed despite nephrologists' desire to treat it. This care gap can be addressed using robust Quality Improvement principles. Our centre will improve obesity management by developing a clinical handbook for nephrologists on how to efficiently address obesity with patients as well as a partnership and streamlined referral process to an obesity specialist. Funding: Clinical Revenue Support
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".