Estimated GFR Reporting Influences Recommendations for Dialysis Initiation
Bibliographic record
Abstract
Automated reporting of estimated GFR (eGFR) with serum creatinine measurement is now common. We surveyed nephrologists in four countries to determine whether eGFR reporting influences nephrologists' recommendations for dialysis initiation. Respondents were randomly allocated to receive a survey of four clinical vignettes that included either serum creatinine concentration only or serum creatinine and the corresponding eGFR. For each scenario, the respondent was asked to rank his or her likelihood of recommending dialysis initiation on a modified 8-point Likert scale, ranging from 1 ("definitely not") to 8 ("definitely would"). Analysis of the 822 eligible responses received showed that the predicted likelihood of recommending dialysis increased by 0.55 points when eGFR was reported (95% confidence interval, 0.33 to 0.76), and this effect was larger for eGFRs >5 ml/min per 1.73 m(2) (P<0.001). Subgroup analyses suggested that physicians who had been in practice ≥13 years were more affected by eGFR reporting (P=0.03). These results indicate that eGFR reporting modestly increases the likelihood that dialysis is recommended, and physicians should be aware of this effect when assessing patients with severe CKD.
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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 | 0.019 | 0.129 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".