Mortality in hemodialysis: Synchrony of biomarker variability indicates a critical transition
Bibliographic record
Abstract
Abstract Critical transition theory suggests that complex systems should experience increased temporal variability just before abrupt change, such as increases in clinical biomarker variability before mortality. We tested this in the context of hemodialysis using 11 clinical biomarkers measured every two weeks in 763 patients over 2496 patient-years. We show that variability – measured by coefficients of variation – is more strongly predictive of mortality than biomarker levels. Further, variability is highly synchronized across all biomarkers, even those from unrelated systems: the first axis of a principal component analysis explains 49% of the variance. This axis then generates powerful predictions of all-cause mortality (HR95=9.7, p<0.0001, where HR95 is a scale-invariant metric of hazard ratio across the predictor range; AUC up to 0.82) and starts to increase markedly ∼3 months prior to death. Such an indicator could provide an early warning sign of physiological collapse and serve to either trigger intervention or initiate discussions around palliative care.
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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 | 0.002 | 0.012 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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 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".