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The Index recomputed live from the record

What the evidence says.What the public feels.

The record holds 935 sourced gains and 771 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.

935 gains
771 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.

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AI gains · 935

397
HealthStableModerate evidence · 1 source

A CECT-based 2PI imaging scoring system combined with clinical parameters enabled a Random Survival Forest model to stratify solitary HCC patients into high- and low-risk recurrence groups and predict postoperative recurrence.

Researchers developed a CECT-based 2PI system that scores imaging features associated with pathological markers and combined it with clinical parameters in machine learning models to predict postoperative recurrence in solitary HCC  5 cm. In 496 patients across primary and external centers, a threshold of  stratified high- versus low-risk groups.

Impact 30%
49
Evidence 25%
95
Scale 20%
35
Confidence 15%
87
Recency 10%
93

Updated Sep 6, 2026 · TRV-2026-0992

68Index score
398
EducationStableModerate evidence · 1 source

Gen Z students in higher education reported optimism that generative AI could improve learning through enhanced productivity, efficiency and personalized learning and expressed intentions to use it for educational purposes.

A November 2023 peer-reviewed study surveyed Generation Z students and Generation X and Generation Y teachers about generative AI in higher education. Gen Z respondents were generally optimistic about benefits such as productivity and personalized learning and said they intended to use the tools for educational purposes, while Gen X and Gen Y teachers acknowledged benefits but reported stronger concerns about overreliance and ethical and pedagogical implications.

Impact 30%
49
Evidence 25%
95
Scale 20%
35
Confidence 15%
87
Recency 10%
93

Updated Sep 5, 2026 · TRV-2026-0988

68Index score
399
HealthStableModerate evidence · 1 source

Cases without consensus were excluded from the AI accuracy analysis. AI accuracy was calculated as the proportion of correct classifications among consensus cases, and the two models were compared using McNemar's test with continuity correction.

Purpose The purpose of this study was to evaluate the performance of two general-use artificial intelligence models, ChatGPT and Grok, in classifying impacted mandibular third molars using the Pell and Gregory system on panoramic radiographs, compared with a resident consensus reference standard. Materials and methods One hundred panoramic radiographic images of impacted mandibular third molars were independently classified by two blinded resident reviewers using the Pell and Gregory classification system.

Impact 30%
49
Evidence 25%
95
Scale 20%
35
Confidence 15%
87
Recency 10%
93

Updated Sep 5, 2026 · TRV-2026-0985

68Index score
400
HealthStableModerate evidence · 1 source

A 17-gene diagnostic signature showed high accuracy for MDS detection (AUC = 0.963). Functional enrichment, machine learning (logistic regression, support vector machine, LASSO), regulatory network (transcription factors, miRNA, RNA-binding proteins, and drug targets), immune infiltration characterization (ssGSEA), and RT-qPCR assessment were performed.

Objective This study aimed to develop a mitochondrial and hematopoiesis-related differentially expressed genes (MH-related DEGs) signature for Myelodysplastic syndromes (MDS) diagnosis and to characterize its regulatory network and immune microenvironment. Methods MH-related DEGs were defined as the intersection of differentially expressed genes from three integrated microarray datasets (GSE145733, GSE19429, GSE81173) with a curated set of mitochondrial- and hematopoiesis-related genes from public databases.

Impact 30%
49
Evidence 25%
95
Scale 20%
35
Confidence 15%
87
Recency 10%
93

Updated Sep 4, 2026 · TRV-2026-0980

68Index score

AI problems · 771

397
LaborRisingModerate evidence · 1 source

People who use AI at work receive negative social evaluations about their competence and motivation, which can harm job candidate assessments.

In four preregistered experiments with 4,439 participants, researchers tested how people who use AI tools at work are perceived. They found that AI users expect to be judged negatively and that observers do rate them lower on competence and motivation, with those judgments spilling over into hiring-related assessments.

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

Updated Jul 24, 2026 · TRV-2026-0525

67Index score
398
LifestyleStableModerate evidence · 1 source

Women reported higher AI anxiety and lower positive attitudes, use, and perceived knowledge of AI, indicating gender-related inequalities in accessing and using AI systems

A May 2025 peer-reviewed study surveyed 335 adults about AI anxiety, attitudes, use, and perceived knowledge. It found women reported higher anxiety and lower positive attitudes, use, and perceived knowledge than men, and that higher anxiety correlated with less positive attitudes overall.

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

Updated Jul 24, 2026 · TRV-2026-0524

67Index score
399
CrimeStableModerate evidence · 1 source

Fraud-detection models trained for specific scam types often fail to generalize to new fraud types and lose effectiveness when trained on outdated data, with inconsistent performance reporting.

On June 13 2025, Crime Science published a systematic literature review of AI and NLP for online fraud detection. The authors screened 2457 records and analyzed 223 studies, mapping data sources, algorithms, and evaluation metrics across 16 fraud types and summarizing best-performing methods for detecting scams in text.

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

Updated Jul 24, 2026 · TRV-2026-0521

67Index score
400
PolicyStableModerate evidence · 1 source

Current GDPR and AI Act definitions of automated decision-making fail to capture real-life applications where automated systems assist rather than replace human decision-makers in migration and asylum.

Published March 2024, this peer-reviewed article analyzes automated systems used in public decision-making for migration, asylum and mobility. It finds that GDPR and AI Act definitions centered on fully automated decisions miss common practices where systems assist human decision-makers, and it proposes a taxonomy to support fundamental rights analysis.

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

Updated Jul 23, 2026 · TRV-2026-0519

67Index score

Recomputed live from the record · Oct 13, 2026, 3:53 AM