Bibliographic record
Abstract
Sir, In the above-mentioned letter, concerns regarding the calibration of creatinine and the ELISA method used to measure cystatin C were raised as a potential drawback of the manuscript. We thank the authors for their constructive criticism. Several points should be raised, however, in support of our conclusion. First, we tested the modified MDRD2-IDMS method, which is suggested as an additional equation for centres that use the Synchron LX20 system to measure their serum creatinine. Secondly, many of the above-tested equations were developed with methods closer to ours than the most recently modified assays. Thirdly, as we have identified in the manuscript, Hallan et al . have demonstrated elegantly that the bias between the creatinine-based methods is lower in patients with higher serum creatinine. Stevens et al . have studied the impact of creatinine calibration on the performance of GFR estimating equations in a pooled individual patient database. They concluded that the effect of calibration was greater at higher levels of GFR. For the C–G equation, calibration worsened the median percentage of difference from −2% to −11.4%. Calibration improved median percentage of difference between measured and estimated GFR by the MDRD2 equation from 9% to 5.8%. A striking finding, however, was their conclusion that calibration could not account for variation in assay performance among individuals. After calibration, larger errors remained for GFR estimates >16 ml/min/1.73 m 2 [ 1 ].
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.019 | 0.029 |
| Insufficient payload (model declined to judge) | 0.008 | 0.010 |
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".