Questions Regarding the Analysis of Long-Term Outcomes in Nephrotic Syndrome
Notice bibliographique
Résumé
We have read with great interest the recent article titled “Long-Term Outcomes in Nephrotic Syndrome by Kidney Biopsy Diagnosis and Proteinuria” published in JASN.1 Although the study provides valuable insights into the long-term outcomes of patients with nephrotic syndrome, we have several concerns regarding the methodology and interpretation of the results, particularly in relation to the adjustment for confounding factors, the dichotomy between pediatric and adult patients, and the static nature of proteinuria metrics used. First, the analysis attributes outcomes primarily to proteinuria levels but inadequately adjusts for critical confounders. Specifically, the study does not account for treatment adherence, which is known to significantly modify proteinuria and disease progression. For instance, previous studies have emphasized that therapy heterogeneity, such as the use of calcineurin inhibitors versus rituximab, significantly affects proteinuria trajectories.2 Without adjusting for these factors, the reported associations may reflect treatment effects rather than intrinsic disease biology. This limitation could lead to misinterpretation of the true relationship between proteinuria levels and long-term outcomes. Second, the reliance on static proteinuria metrics, such as “lowest proteinuria” and time-averaged values, overlooks the dynamic variability within individuals. The study's capture of the dynamic changes in proteinuria is still not comprehensive and in-depth enough, and it has not adequately considered the effect of short-term rapid changes or fluctuations in proteinuria on the prognosis. For example, transient remission followed by relapse is a hallmark of minimal change disease (MCD), but this pattern is not captured by the static metrics used in the study. Dynamic proteinuria patterns have been validated as superior prognostic markers in other glomerular diseases, such as IgA nephropathy.3 By ignoring these dynamic patterns, the study may misclassify patients' risk and provide an incomplete picture of their disease course. More importantly, the fact that the time points for proteinuria measurement in the study (such as 6–12 and 6–24 months after onset) do not match the critical stages of the disease. For patients with rapidly progressive disease, these time points may fail to accurately reflect the activity of the disease in its early stages or the stability in its later stages, leading to a misunderstanding of the relationship between proteinuria and disease progression. For example, if a patient is already in a stage of rapid deterioration of kidney function 6 months after onset, the level of proteinuria at that time may be more influenced by the decline in kidney function, rather than simply reflecting disease activity. Moreover, the study focuses solely on the quantity of proteinuria, without considering the effect of differences in its composition on kidney function impairment and disease prognosis. This may lead to a one-sided assessment of the harmful effects of proteinuria. For example, large-molecule proteinuria and small-molecule proteinuria have different mechanisms and degrees of damage to the renal tubules and interstitium. However, the study fails to distinguish these differences and thus cannot accurately assess the effect of different types of proteinuria on kidney function. In conclusion, although the study provides important data on long-term outcomes in nephrotic syndrome, the limitations mentioned above could affect the validity and generalizability of the findings. Future research should consider more comprehensive adjustments for confounding factors and incorporate dynamic proteinuria metrics to better reflect the true nature of disease progression.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,241 | 0,520 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,004 | 0,006 |
| Bibliométrie | 0,003 | 0,007 |
| Études des sciences et des technologies | 0,002 | 0,012 |
| Communication savante | 0,005 | 0,012 |
| Science ouverte | 0,010 | 0,005 |
| Intégrité de la recherche | 0,007 | 0,016 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».