Unsupervised analysis of combined lipid and coagulation data reveals coagulopathy subtypes among dialysis patients
Bibliographic record
Abstract
Hemodialysis (HD) and peritoneal dialysis (PD) are the primary means of managing end stage renal disease (ESRD). However, these treatment modalities are associated with the onset of coagulation abnormalities. Effective management of coagulation risk among these patients requires the identification of surrogate markers that provide an early indication of the coagulation abnormalities. The role of sphingolipids in the manifestation and prediction of coagulation abnormalities among dialysis patients have never been investigated. Herein, we report the first instance of an in depth investigation into the sphingolipid changes among ESRD patients undergoing HD and PD. The results reveal distinct differences in terms of perturbations to specific sphingolipid biosynthetic pathways that are highly dependent on the treatment modality. Our studies also demonstrated strong correlation between specific sphingolipids and coagulation parameters, such as HexCer(d18:1/26:0) and maximal amplitude (MA), SM(d18:1/24:1) and tissue factor pathway inhibitor, and sphingosine 1-phosphate d18:1 and FX (Spearman ρ of 0.93, 0.89, and −0.89, respectively). Furthermore, our study revealed the potential for using HexCer(d18:1/22:0), HexCer(d18:1/24:0), and HexCer(d18:1/26:0) (r 2 = 0.71, 0.82, and 0.63, respectively) and coagulation parameter MA (r 2 = 0.7) for successful diagnosis of differential coagulopathies among ESRD patients undergoing HD, providing an opportunity toward personalized disease management.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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 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".