Unphysiology Is the Major Factor Influencing Cardiovascular Instability during Hemodialysis
Bibliographic record
Abstract
Background: Hemodialysis is often complicated by cardiovascular instability (CVI). We studied factors contributing to this problem during 720 hemodialyses (HDs) in 20 patients; 480 dialyses were 6/week and 240 were 3/week. Methods: Dependent variables were increase in pulse rate (PR) and maximal (MAX) and overall (OV) fall of systolic blood pressure (BP). Independent variables were dialyses/week (DIAL), ultrafiltration (Uf), % of body weight (BW), pre‐post BUN (ΔBUN), time on dialysis (T), speed of dialysis (K/V in mL min–1 kg–1 BW), target‐postdialysis BW (Ta‐Po BW), Kt/V, ΔPO4, Δbicarbonate, Δpotassium, ΔBUN, an ‘unphysiology index’ summing up changes in electrolytes, and BUN and BW during dialysis (UPI). The relations were analyzed by backward multiple regression analysis. Results: PR increased 0.5 ± 11/min; MAX BP fall was 23 ± 17 mmHg; OV BP fall was 12 ± 19 mmHg. In multiple stepwise backward regression analysis, independents in order of importance: PR = 38 – DIAL × 4 + T × 0.1 + Uf × 1.8 +ΔPO4 × 1.8 – UPI× 0.2 – K/V × 2, r = 0.30, p < 0.0001; MAX BP = UPI × 0.4 – ΔBUN × 0.3 + ΔPO4 × 2.6 + 11, r = 0.34, p < 0.0001; OV BP = UPI × 0.4 – ΔBUN × 0.3 +ΔPO4 × 2.7 + 1, r = 0.33, p < 0.0001. Conclusion: To prevent BP fall and tachycardia during hemodialysis, the most important factor to decrease is unphysiology, i.e., the oscillations in electrolytes, fluid spaces, and osmolality that occur during dialysis. The best way to do this is to dialyze patients daily. An unexpected finding worthy of further investigation was the large detrimental influence of ΔPO4 on CVI.
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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 | 0.003 |
| 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".