Abstracts from the 6th Joint Meeting of ABCD & UKKA 2023
Bibliographic record
Abstract
Objectives: Globally, 40% of people receiving peritoneal dialysis have a diagnosis of diabetes.High-quality data on the potential impact of improving glycaemic control are needed to inform the KDIGO recommendation to individualise HbA1c targets.Methods: The association between first HbA1c and all-cause mortality in people on peritoneal dialysis (PD) for kidney failure recruited into PDOPPS1(2014-2017) and PDOPPS2(2018-2022) identified as diabetic was estimated using Cox proportional hazards models adjusted for age, sex, race, country, albumin, haemoglobin and co-morbidities.To inform HbA1c individualisation, subgroup analyses drawn from these adjustment variables were performed.Results: From 24,259 individuals recruited into PDOPPS, 13,646 were identified as diabetic.Of these, 9,722 had HbA1c performed after a mean of 11.2 months' PD therapy, mean follow-up 17.2 months.Mean HbA1c was 6.9%.Mean HbA1c ranged from 6.4% in Japan to 7.3% in Canada.In people with type 2 diabetes (T2DM), relative to HbA1c 6.0-7.0%,there was weak evidence for increased all-cause mortality for HbA1c >9% (HR 1.18, p=0.067), becoming more robust in those aged <65 years (HR 1.4, p=0.01).In individuals with albumin >3.0g/dL, or those with no previous coronary artery disease (CAD), the threshold for significantly increased mortality dropped to >8% (HR ~1.2 ).The hazard ratio for mortality for the >8% threshold climbed to 1.92 for those aged <65 years, with both serum albumin >3.0g/dL and no previous CAD.Conclusions: In diabetic adults receiving PD for kidney failure, the associations seen between HbA1c and mortality argue for tighter individualised targets for particular patient subgroups (younger, non-inflamed and without established CAD) than clinical practice guidelines have previously suggested.
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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) | 0.000 | 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) | 0.000 | 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".