Polyneuropathy in Kidney Transplant Recipients: Accuracy of a New Clinical Diagnostic Scoring System
Bibliographic record
Abstract
ABSTRACT Background and Aims Polyneuropathy is highly prevalent among kidney transplant recipients (KTR), underscoring the need for an accurate yet easy‐to‐perform diagnostic method to improve understanding and enable early identification of treatable cases. Methods This study included KTR at least 12 months post‐transplant at the University Medical Centre Groningen, the Netherlands. An expert panel assessed polyneuropathy through a structured neurological examination, quantitative sensory testing, and nerve conduction studies. The modified Toronto Clinical Neuropathy Score (mTCNS) was obtained from all participants. Logistic regression analyses with Firth penalization validated the mTCNS components. A new model, the Kidney Transplant Neuropathy Score (KTNS), was developed through stepwise elimination. Diagnostic performance was evaluated with bootstrapped metrics and ROC curve analyses. Results Among 160 KTR, 91 (57%) were diagnosed with polyneuropathy. All 10 mTCNS components were univariably associated with polyneuropathy; numbness (OR = 4.9 [1.8–18.0]), tingling (OR = 2.5 [1.2–5.9]), impaired nociception (OR = 1.5 [1.1–2.2]), and reduced vibration perception (OR = 1.5 [1.0–2.4]) remained independently associated in multivariable analysis. The mTCNS achieved an area under the curve (AUC) in ROC analysis of 0.83 [0.76–0.89]. Two KTNS were derived: the KTNSBasic, including history of numbness, tingling in the feet, and pinprick and vibration perception testing (AUC–ROC: 0.85 [0.79–0.90]); and the KTNSAdvanced, replacing vibration perception with Achilles and patellar deep tendon reflex testing (AUC–ROC: 0.90 [0.85–0.94]). Interpretation The mTCNS is a valid diagnostic tool for polyneuropathy in KTR. The KTNS offers a simplified alternative based on key symptoms and sensory tests, with reflex testing included in the KTNSAdvanced for settings with neurological expertise. Trial Registration ClinicalTrials.gov identifier: NCT04664426
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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 | 0.008 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".