± HEALTH In our modern society, digital devices, social media platforms, and artificial intelligence (AI) tools have b…+ CLIMATE The growing complexity of electric vehicle charging station (EVCS) operations—driven by grid constraints, ren…± HEALTH Objectives This study aim to develop, compare and internally validate machine-learning models for predicting…+ HEALTH Diabetes management faces a number of obstacles, such as fragmented healthcare data, privacy concerns, poor e…− SCIENCE Background Craniofacial soft tissue thickness (CFSTT) is a critical parameter in forensic facial reconstructi…+ HEALTH Background Technological innovation in total joint arthroplasty (TJA) has largely focused on intraoperative p…± HEALTH Infective endocarditis (IE) continues to be an often fatal condition despite improvements in cardiac surgical…± HEALTH Purpose of review Systemic sclerosis-associated interstitial lung disease (SSc-ILD) is the leading cause of d…
TruaceTracing the truth around AISaturday, September 12, 2026
Subscribe
TRV-2026-1025Certified recordPeer-reviewed

An integrative clinical-molecular model as an auxiliary predictive tool for glioma malignancy grade

Objective An integrative auxiliary predictive model incorporating clinical parameters, serum biomarkers, and molecular pathological markers was developed to assess glioma malignancy grade. Methods This single-center retrospective observational study consecutively enrolled 400 glioma patients. A total of 26 variables, including demographic characteristics, clinical parameters, laboratory indicators, and serum biomarkers, were analyzed. Predictors were selected using univariate analysis, followed by Least Absolute…

Health · The Trace — both readings · certified 2026-09-09 · v1 · article view · machine-readable

Current reading — gain

A Random Forest model integrating age, KPS, tumor diameter, NLR, AGR, IDH status and Ki-67 achieved AUC 0.864 training and 0.820 validation to assist preoperative assessment of high-grade glioma.

Current reading — problem

The auxiliary model cannot replace pathological and molecular diagnosis and relies on tissue-derived IDH and Ki-67 markers, with development limited to a single-center retrospective cohort of 400 patients and internal validation only.

What this doesn’t fix

Model is exploratory, single-center retrospective with internal validation only, and cannot replace pathological and molecular diagnosis because it depends on tissue-derived markers IDH and Ki-67.

Evidence

Reader signal

How should this claim be treated?

Cite this record

Truvace Impact Record TRV-2026-1025, v1: “An integrative clinical-molecular model as an auxiliary predictive tool for glioma malignancy grade.” Truvace, 2026-09-09. /record/TRV-2026-1025 (accessed at citation time). sha256 cdf20d9265c6beb7…

Calibration history

Every change to this record since certification, in the open. None yet — the reading has held since it entered the record.

  1. Certifiedv1cdf20d9265c6…

    Certified into the record

Verify this record
How to verify without trusting this page

Fetch the canonical text of any version from /api/record/TRV-2026-1025 and hash it yourself — for example shasum -a 256 on the saved canonical field. The result must equal content_hash, and each version’s text ends with prev:followed by the prior version’s hash (version 1 chains to 64 zeros). If a single character of any version had been altered since certification, the chain would not reproduce.