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 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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.485 | 0.319 |
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".