<i>Kt/V</i>: achievement, predictors and relationship to mortality in hemodialysis patients in the Gulf Cooperation Council countries: results from DOPPS (2012–18)
Bibliographic record
Abstract
Abstract Background Dialysis adequacy, as measured by single pool Kt/V, is an important parameter for assessing hemodialysis (HD) patients’ health. Guidelines have recommended Kt/V of 1.2 as the minimum dose for thrice-weekly HD. We describe Kt/V achievement, its predictors and its relationship with mortality in the Gulf Cooperation Council (GCC) (Bahrain, Kuwait, Oman, Qatar, Saudi Arabia and the United Arab Emirates). Methods We analyzed data (2012–18) from the prospective cohort Dialysis Outcomes and Practice Patterns Study for 1544 GCC patients ≥18 years old and on dialysis >180 days. Results Thirty-four percent of GCC HD patients had low Kt/V (<1.2) versus 5%–17% in Canada, Europe, Japan and the USA. Across the GCC countries, low Kt/V prevalence ranged from 10% to 54%. In multivariable logistic regression, low Kt/V was more common (P < 0.05) with larger body weight and height, being male, shorter treatment time (TT), lower blood flow rate (BFR), greater comorbidity burden and using HD versus hemodiafiltration. In adjusted Cox models, low Kt/V was strongly related to higher mortality in women [hazard ratio (HR) = 1.91, 95% confidence interval (CI) 1.09–3.34] but not in men (HR = 1.16, 95% CI 0.70–1.92). Low BFR (<350 mL/min) and TT (<4 h) were common; 41% of low Kt/V cases were attributable to low BFR or TT (52% for women and 36% for men). Conclusion Relatively large proportions of GCC HD patients have low Kt/V. Increasing BFR to ≥350 mL/min and TT to ≥4 h thrice weekly will reduce low Kt/V prevalence and may improve survival in GCC HD patients—particularly among women.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".