Bibliographic record
Abstract
Sir, We thank Delanaye et al. for raising certain remarks on our methodology that led, in their conclusion, to an analytical limitation of our study. These limitations pertain to a standardization of serum creatinine and the ELISA method used to measure cystatin C. They concluded that more studies seem necessary on this topic, with improved methodology, notably from an analytical point of view. As for a standardization of serum creatinine, we believe that this is not the case obviously for all equations other than the MDRD equation. MDRD2 (IDMS) has been suggested as an alternative for labs that do not standardize their method to measure serum creatinine. We therefore included the modified MDRD2 (IDMS) for this purpose. We hope that we have emphasized this limitation and presented an argument from the literature (Hallan et al. , Am J Kidney Dis 2004; 44: 84), who pointed out that the bias due to a missing calibration decreases as serum creatinine increases, as in kidney transplant patients.
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 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.037 | 0.035 |
| Insufficient payload (model declined to judge) | 0.018 | 0.013 |
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".