Interdialytic weight gain and ultrafiltration rate in hemodialysis: Lessons about fluid adherence from a national registry of clinical practice
Bibliographic record
Abstract
Excessive interdialytic weight gain (IWG) and ultrafiltration rates (UFR) above 10 mL/h/kg body weight imply higher morbidity and mortality. This study aimed to estimate the prevalence of high fluid consumers, describe UFR patterns, and describe patient characteristics associated with IWG and UFR. The Swedish Dialysis DataBase and The Swedish Renal Registry of Active Treatment of Uremia were used as data sources. Data were analyzed from patients aged >/=18 on regular treatment with hemodialysis (HD) and registered during 2002 to 2006. Interdialytic weight gain and dialytic UFR were examined in annual cohorts and the records were based on 9693 HD sessions in 4498 patients. Differences in proportions were analyzed with the chi-square test and differences in means were tested using the ANOVA or the t test. About 30% of the patients had IWG that exceed 3.5% of dry body weight and 5% had IWG >/=5.7%. The volume removed during HD was >10 mL/h/kg for 15% to 23% of the patients, and this rate increased during the first dialytic year. Patient characteristics associated with fluid overload were younger age, lower body mass index, longer dialytic vintage, and high blood pressure. By studying IWG and dialytic UFR as quality indicators, it is shown that there is a potential for continuing improvement in the care of patients in HD settings, i.e., to enhanced adherence to fluid restriction or alternatively to extend the frequency of dialysis for all patients, e.g., by providing daily treatment.
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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 | 0.007 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".