Physical examination for the detection of hypervolemia among patients on chronic dialysis: A diagnostic‐test study
Bibliographic record
Abstract
INTRODUCTION: Assessment of dry-weight among patients on dialysis is challenging in the absence of reliable markers to define fluid overload (FO). This study aimed to explore the value of two simple clinical signs, pedal edema, and crackles at pulmonary auscultation, in diagnosing hypervolemia, using bioimpendence spectroscopy (BIS) as reference standard. METHODS: In a cohort of 107 asymptomatic dialysis patients, FO was assessed with physical examination and BIS shortly before the mid-week dialysis session. Patients were also asked to perform home blood pressure (BP) monitoring with a validated, automatic device (HEM-705, Omron, Healthcare) for 1 week in order to determine their BP outside of dialysis. FINDINGS: Patients within the high tertile of predialysis FO had longer dialysis vintage, lower serum albumin and higher home systolic BP, despite the more aggressive treatment with a higher average number of antihypertensives daily. In receiver-operating-characteristic (ROC) curve analysis, pedal edema (area under curve [AUC]: 0.534; 95% confidence interval [CI]: 0.416-0.651) and pulmonary crackles (AUC: 0.551; 95% CI: 0.432-0.671) had limited accuracy in detecting excess predialysis FO > 2.2 L. The agreement of pedal edema (k-coefficient: 0.065) and pulmonary crackles (k-coefficient: 0.122) with BIS-derived FO was poor. In multivariate linear regression analysis, longer dialysis vintage (β: 0.306, p < 0.001) and higher home systolic BP (β: 0.287, p < 0.01) were the two factors that were associated with predialysis FO. CONCLUSIONS: This study showed that among asymptomatic dialysis patients, pedal edema and pulmonary crackles in physical examination had limited discriminatory power in detection of FO, as assessed with the method of BIS.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".