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The Index recomputed live from the record

What the evidence says.What the public feels.

The record holds 939 sourced gains and 772 sourced problems, averaging 68 and 66 on the index score. Readers have logged 22 public signals on the Pulse, which is kept apart and never counted as evidence.

939 gains
772 problems
Every sourced claim in the Index, one square each, shaded by the strength of its evidence. High Moderate Emerging

Ranks distinct AI gain and problem claims from the published record. Scores reward impact, independent source strength, scale, confidence, and recency.

1,711 results
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AI gains · 939

457
HealthStableModerate evidence · 1 source

A 16-gene macrophage-associated signature developed with ridge regression and XGBoost stratified HCC patients and achieved high diagnostic discrimination, with LGALS1 emerging as a top feature linked to survival and treatment response.

On August 22, 2026, researchers reported an integrative analysis combining single-cell RNA-sequencing with bulk transcriptomic and clinical data from TCGA-LIHC, GEO, and ICGC to find macrophage-associated biomarkers in hepatocellular carcinoma. They derived a 16-gene signature that stratified tumors by survival and clinicopathological features, with ridge regression showing AUC 0.988 in TCGA-LIHC and 0.895-0.924 externally, and XGBoost SHAP identifying LGALS1 as highly informative.

Impact 30%
49
Evidence 25%
95
Scale 20%
35
Confidence 15%
87
Recency 10%
90

Updated Aug 24, 2026 · TRV-2026-0865

68Index score
458
HealthStableModerate evidence · 1 source

An interpretable logistic regression model trained on YRBS data predicted lifetime marijuana use among male high school students with high accuracy, enabling early identification of at-risk boys for targeted school and community prevention.

A cross-sectional study of 8285 male high school students from the 2023 Youth Risk Behavior Surveillance System used 36 demographic, behavioral, and psychosocial predictors to model lifetime marijuana use, reported by 28.4% of participants. After benchmarking 17 algorithms, an optimized logistic regression model with SHAP and LIME explanations achieved AUC 0.9034 and accuracy 0.8582.

Impact 30%
49
Evidence 25%
95
Scale 20%
35
Confidence 15%
87
Recency 10%
90

Updated Aug 24, 2026 · TRV-2026-0864

68Index score
459
EducationStableModerate evidence · 1 source

AI tools integrated into radiation therapy workflows can improve efficiency by automating organ-at-risk contouring tasks.

This peer-reviewed educational perspective from August 2026 examined how organ-at-risk contouring is taught in an Australian undergraduate radiation therapy program as AI auto-contouring enters clinical workflows. The authors reviewed curriculum scope and technologies and examined students' preferred methods, confidence across OARs, and perceived factors affecting quality.

Impact 30%
49
Evidence 25%
95
Scale 20%
35
Confidence 15%
87
Recency 10%
90

Updated Aug 24, 2026 · TRV-2026-0863

68Index score
460
HealthStableModerate evidence · 1 source

In 2230 MIMIC-IV adults with AKI, higher TyG-FI was independently associated with ICU and 28-day mortality and enabled identification of lower-burden versus high frailty-organ dysfunction phenotypes that reproduced in 1831 eICU patients with high agreement.

Researchers derived and tested the triglyceride-glucose frailty index in 2230 MIMIC-IV adults with KDIGO-defined AKI, examining associations with ICU, in-hospital, 28-day, 90-day and 365-day mortality, identifying two consensus phenotypes, and evaluating 12 prediction algorithms with SHAP and LIME interpretation and external validation in 1831 eICU patients.

Impact 30%
49
Evidence 25%
95
Scale 20%
35
Confidence 15%
87
Recency 10%
90

Updated Aug 24, 2026 · TRV-2026-0862

68Index score

AI problems · 772

457
EducationStableModerate evidence · 1 source

University students reported privacy concerns, technophobia, and guilt feelings that reduced behavioral intention to adopt ChatGPT for learning.

Published June 2, 2024, this peer-reviewed study explored why university students adopt ChatGPT, examining how self-learning capabilities affect knowledge acquisition and application, how personalization relates to novelty value and benefits, and how individual impact, innovativeness, and barriers shape behavioral intention and actual use.

Impact 30%
49
Evidence 25%
95
Scale 20%
35
Confidence 15%
87
Recency 10%
84

Updated Jul 20, 2026 · TRV-2026-0408

67Index score
458
ScienceStableModerate evidence · 1 source

Despite parallels to coding, LLMs still struggle with formalized mathematics, where proof synthesis remains brittle and advances have been significantly more challenging.

As of January 2026, this peer-reviewed review surveys Large Language Models applied to mathematics in both natural-style language and formal symbolic syntax suitable for automatic verification. It notes coding has emerged as a successful application of structured reasoning, while formalized mathematics has proven significantly more challenging.

Impact 30%
49
Evidence 25%
95
Scale 20%
35
Confidence 15%
87
Recency 10%
84

Updated Jul 20, 2026 · TRV-2026-0405

67Index score
459
HealthStableModerate evidence · 1 source

When compared to historical multidisciplinary routine practice readings in 1000 testing cases, the AI system did not demonstrate confirmed non-inferiority and showed slightly lower specificity at matched sensitivity.

Researchers trained an AI system on 9207 prostate MRI examinations from the Netherlands and tested it on 1000 examinations from the Netherlands and Norway, with a 400-case subset read by 62 radiologists from 20 countries. By June 2024 publication, the AI achieved AUROC 0.91 versus 0.86 for radiologists using PI-RADS 2.1, and at matched operating points detected 6.8% more clinically significant cancers at same specificity or 50.4% fewer false positives at same sensitivity.

Impact 30%
49
Evidence 25%
95
Scale 20%
35
Confidence 15%
87
Recency 10%
84

Updated Jul 20, 2026 · TRV-2026-0402

67Index score
460
EducationStableModerate evidence · 1 source

Deploying AI in education faces key challenges of ensuring privacy and ethical use, trustworthy algorithms, and equity and fairness.

Published June 4, 2024, this peer-reviewed paper examined AI in education through a Delphi study of 33 international professionals plus follow-up face-to-face discussions with international researchers. It found that effective use depends on keeping humans in the loop rather than blindly replacing human involvement.

Impact 30%
49
Evidence 25%
95
Scale 20%
35
Confidence 15%
87
Recency 10%
84

Updated Jul 20, 2026 · TRV-2026-0401

67Index score

Recomputed live from the record · Oct 13, 2026, 9:29 AM