The Association of eGFR Reporting with the Timing of Dialysis Initiation
Bibliographic record
Abstract
Automated reporting of eGFR by laboratories has been widely implemented during the last decade. Over this same period, a steady increase in eGFR at dialysis initiation has been reported. This study examined trends in eGFR at dialysis initiation over time among incident dialysis patient populations before and after eGFR reporting. All patients who initiated dialysis between January of 2001 and December of 2010 in four Canadian provinces that implemented province-wide automated eGFR reporting and had an eGFR measure at dialysis initiation were included in the study (n=22,208). The primary outcome was change over time in eGFR among patients at dialysis initiation. An interrupted time series and adjusted multilevel regression models were used to determine the differences in eGFR at dialysis initiation before and after reporting. We observed a linear increase in the mean eGFR at dialysis initiation from 9.1 to 10.8 ml/min per m(2) during the study period. There was no change in the trajectory of the eGFR at dialysis initiation before or after eGFR reporting in crude or adjusted models accounting for case mix and facility characteristics. These findings were consistent among age and sex strata and when the proportions of patients with an eGFR≥10.5 or ≥12 ml/min per m(2) were examined. In conclusion, automated laboratory-based eGFR reporting did not influence eGFR at dialysis initiation among incident dialysis patient populations. Concerns that widespread eGFR reporting leads to earlier dialysis initiation are not supported by this study.
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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 | 0.006 | 0.041 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".