Stress Is the Strongest Predictor of Death in Hemodialysis and Can Be Measured
Bibliographic record
Abstract
The overall results of hemodialysis are not good with a 90%, 5-year death rate in most Western countries. Several self-evident variables: age, diagnosis, and comorbidity predict death but there are inexplicable individual variations. The usual measure of hemodialysis, “Kt/V” has been useless in predicting mortality. We speculated that it would be most important to study individual stress reactions to dialysis. We measured two important peptides measuring stress: ANP and NPY and created a stress index = (ANP + 3 × NPY). ANP reacts to the stress of fluid overload and NPY reacts to fluid overload, blood pressure and nervous overactivity. The stress index was measured in 33 patients that were followed for up to 12 years. All 12 patients (HSI) with a stress index above the mean of 275 ng/l died compared to 6/21 of patients (LSI) with an index below mean p < 0.0001. 50% survival HIS was at 29 months, compared to 116 months for LSI, p = 0.003. The following factors were correlated to the stress index: R p Age 0.4 0.026 MAP 0.5 0.005 Weight gain 0.4 0.036 Heart volume 0.4 0.010 Heart ailure 0.6 < 0.0001 Ischemic HD 0.6 < 0.0001 Not correlated were albumin, Kt/V, BMI, LVH, Hgb, time on dialysis. In stepwise Cox proportional hazard analysis with all of the above factors, significant in predicting survival as co-variates were the stress index, albumin and heart volume. Conclusions: Stress index captures the combined influence of age, fluid overload, MAP, HV, HF, IHD and is the most significant of all variables associated with death. In hemodialysis patients with a high stress index one should modify dialysis; daily, longer, and slower hemodialysis should be strongly considered. Conventional, fast three times per week is contraindicated in such patients.
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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 | 0.000 | 0.002 |
| 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) | 0.001 | 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".