Redefining Overweight and Obesity (OW/OB) in a Large Cohort of Patients with ADPKD
Bibliographic record
Abstract
Background: Emerging data indicate OW/OB are risk factors for accelerated progression in ADPKD patients. Thus, an accurate diagnosis is crucial to implement appropriate therapeutic measures and avoid unnecessary treatments if not indicated. The traditional BMI formula used to define OW/OB does not take into account the excess of weight attributed to large cystic kidney and liver in ADPKD. Here we define the prevalence and clinical characteristics of ADPKD patients with OW/OB in a large cohort using a formula recommended by KDIGO Methods: Cross-sectional analysis of patients with PKD1 and PKD2 mutations from the Toronto PKD Registry who had clinical and MRI measurements of total kidney volume (TKV) and total liver volume (TLV) was performed. BMI was calculated by the formula: weight (kg)/height (m2)), while estimated BMI (eBMI) was calculated with the formula proposed by KDIGO: adjusted body weight (body weight (kg) - TKV (kg) - TLV (kg) + weight of normal kidneys and liver/ height (m2) Results: Table 1 shows clinical characteristics of the study cohort (n=693) and subgroups defined by eBMI category. The median weight and BMI pre and post adjustment were 73 kg and 25,1 kg/m2 and 71.8 kg, and 24,3 kg/m2 respectively. A total of 352 (50.7%) patients were initially classified as OW/OB but the number decreased to 314 (45.3%) post adjustment; 10.7% of those initially classified as OW/OB were reclassified as having normal weight, and 80 (11.5%) of the study cohort were classified into a milder BMI category post adjustment Conclusion: OW/OB are highly prevalent among patients with ADPKD and are associated with worse prognoses. Using eBMI for an accurate diagnosis of OW/OB can help avoid unnecessary treatment for those previously misclassified
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".