Validation of the Toronto Formula to Predict Progression in IgA Nephropathy
Bibliographic record
Abstract
<i>Background/Aim:</i> Predicting outcome in IgA nephropathy (IgAN) is difficult. The Toronto formula uses average mean arterial blood pressure and proteinuria during the first 2 years of follow-up (MAP<sub>0–2</sub>, UP<sub>0–2</sub>) to predict the subsequent slope of estimated creatinine clearance (eCrCl). We aimed to validate the Toronto formula in a Scottish cohort and test the hypothesis that adding the slope eCrCl over the first 2 years of follow-up (eCrCl<sub>0–2</sub>) would improve the predictive utility of a similar multivariate model. <i>Methods:</i> Adultsfrom our centre with biopsy-proven IgAN (n = 169) and at least 2 years of follow-up (median 129.4 months) were included. Clinical data were used to calculate MAP<sub>0–2</sub>,UP<sub>0–2</sub>,slope eCrCl<sub>0–2 </sub>and predicted slope eCrCl (using the Toronto formula). <i>Results:</i> There was a significant correlation between predicted slope eCrCl using the Toronto formula and actual slope eCrCl (R<sup>2 =</sup> 0.21; p < 0.001). The formula predicted the actual rate of progression to within 4 ml/min/year in 75% of subjects, predicting patients with the most rapid deterioration with the greatest accuracy. The multivariate linear regression model created in our cohort using the same independent variables as the Toronto formula to predict the overall slope eCrCl had an R<sup>2</sup> of 0.22 (p < 0.001) and adding the slope CrCl<sub>0–2</sub> only increased this to 0.25. <i>Conclusions:</i> The Toronto formula is valid in a European population and useful for identifying patients at high risk of future deterioration in renal function. Adding slope eCrCl<sub>0–2</sub> to a predictive model containing MAP<sub>0–2</sub>, andUP<sub>0–2 </sub>does not appear to improve prediction of the overall slope of eCrCl.
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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 | 0.009 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".