Abstracts of the 5<sup>th</sup> Cachexia Conference, Barcelona, Spain, December 5–8, 2009
Bibliographic record
Abstract
Background and aims: Malnutrition is associated with higher levels of morbidity and mortality in patients with CKD. The aim of this study was to determine the prevalence of protein-energy wasting (PEW) using new diagnostic criteria compared with ICD-10 AM definitions in patients with CKD. Methods: Forty-two patients (22 male, 20 female; mean age, 65.7 (SD 17.6)years) from a private haemodialysis unit participated. To determine PEW, four categories are assessed: biochemistry serum albumin (<38 mg/l) or serum cholesterol (<100 mg/100 ml); body mass index (BMI) <23 or total body fat <10%; muscle mass, reduced mid-arm muscle mass area; and reduced dietary intake, protein <0.8 g/kg/day or energy <100 kJ/kg/day. At least three of the four categories must be present to diagnose PEW. The ICD-10 AM definitions of malnutrition include BMI <18.5 or evidence of weight loss, decreased intake and presence of fat loss and muscle wasting. This was assessed using subjective global assessment. Dietary intake was analysed using Foodworks (version 5). Fat mass was determined using bioelectrical impedance spectroscopy. Results: Ten patients (24%) met the criteria for PEW compared to eight patients (19%) using the ICD-10 AM criteria; however, only three patients met the criteria for both definitions. Thirteen patients had low biochemistry; 14 had low BMI; 19 had low muscle mass; 21 had low intake; two had BMI <18.5; and six had evidence of weight loss, decreased dietary intake, presence of subcutaneous fat loss and muscle wasting. Time taken for the PEW diagnosis was considerably longer than ICD-10 AM. Conclusions: The new diagnostic criteria for PEW require further validation studies in patients with CKD.
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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 | 0.002 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".