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The Index recomputed live from the record

What the evidence says.What the public feels.

The record holds 933 sourced gains and 770 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.

933 gains
770 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 · 933

177
HealthStableModerate evidence · 1 source

YOLOv8x-pose and YOLOv8x-seg models automated landmark detection and apical segmentation for the Cameriere European method in children aged 5-13, achieving high detection accuracy and low measurement error in a first-stage validation.

This first-stage retrospective validation evaluated YOLOv8-based models to automate the anatomical inputs for the Cameriere European dental age estimation method using 4,050 panoramic radiographs of children aged 5-13. A YOLOv8x-pose model detected open-apex landmarks and a YOLOv8x-seg model segmented closed apices, with performance compared to manual reference annotations from CranioCatch software.

Impact 30%
63
Evidence 25%
95
Scale 20%
35
Confidence 15%
87
Recency 10%
88

Updated Aug 9, 2026 · TRV-2026-0715

72Index score
178
HealthStableModerate evidence · 1 source

ChatGPT-5.0 achieved 75.77% accuracy and the highest sensitivity at 76.68% for orthodontic extraction decisions, performing comparably to XGBoost and significantly better than random forest, SVM, logistic regression and MLP.

A comparative study published August 7, 2026 evaluated ChatGPT-5.0 against five supervised machine learning algorithms for orthodontic extraction decisions. Using 520 cases (42.88% extraction, 57.12% non-extraction) and 23 clinical, cephalometric and photographic variables, with expert consensus as reference, ChatGPT-5.0 achieved 75.77% accuracy and 76.68% sensitivity under 5-fold cross-validation, compared to 78.08% accuracy for XGBoost.

Impact 30%
63
Evidence 25%
95
Scale 20%
35
Confidence 15%
87
Recency 10%
88

Updated Aug 8, 2026 · TRV-2026-0689

72Index score
179
HealthStableModerate evidence · 1 source

K-means clustering of eight biopsychosocial variables identified three distinct AUD profiles that predicted 3-month abstinence, with Late-Onset achieving 65.8% abstinence, supporting stratified front-loaded intervention.

In a retrospective study of 102 patients at a tertiary care center in India, researchers used k-means clustering on eight biopsychosocial baseline variables to derive three AUD profiles. By the August 2026 publication date, they reported Late-Onset, High-Functioning, and Severe groups with differing 3-month abstinence rates corroborated by GGT levels and bootstrap-assessed cluster stability.

Impact 30%
63
Evidence 25%
95
Scale 20%
35
Confidence 15%
87
Recency 10%
88

Updated Aug 8, 2026 · TRV-2026-0687

72Index score
180
HealthStableModerate evidence · 1 source

AI systems are being used in urology and hospital care to support image interpretation, risk stratification, clinical decision-making, documentation, and workflow optimization.

A July 2026 review in Die Urologie describes artificial intelligence moving from research into everyday clinical practice and hospital care, with generative AI and large language models now used alongside established image-analysis tools for documentation, knowledge management, patient communication, and workflow optimization, plus AI-assisted radiological and pathological interpretation and risk stratification.

Impact 30%
49
Evidence 25%
95
Scale 20%
60
Confidence 15%
87
Recency 10%
86

Updated Jul 31, 2026 · TRV-2026-0601

72Index score

AI problems · 770

177
HealthNewly addedModerate evidence · 1 source

In highly selective PCI settings, conventional ATE estimation for abciximab suffers from lack of practical positivity, forcing unstable extrapolation and assuming patients with near-certain treatment probability could realistically be assigned to withhold therapy.

Researchers illustrated incremental propensity score interventions using 996 patients undergoing percutaneous coronary intervention, estimating propensity scores and outcomes with a Super Learner ensemble and 10-fold splitting to evaluate shifting each patient's probability of receiving abciximab on six-month mortality.

Impact 30%
49
Evidence 25%
95
Scale 20%
35
Confidence 15%
87
Recency 10%
98

Updated Oct 1, 2026 · TRV-2026-1233

68Index score
178
HealthNewly addedModerate evidence · 1 source

In the same RCT simulations, the standard log-rank splitting rule for random survival forest was outperformed by alternative rules in nonproportional hazards settings, limiting its predictive advantage.

By 2026-10-01, authors reported a neutral comparison of Cox proportional hazards regression and random survival forest for predicting patient-specific survival probabilities, using varied simulation scenarios based on two RCT reference datasets and TRIPOD-aligned metrics. They observed that C-index-only conclusions did not generalize, that overall performance measures appeared more reasonable, and that splitting rule choice mattered for RSF.

Impact 30%
49
Evidence 25%
95
Scale 20%
35
Confidence 15%
87
Recency 10%
98

Updated Oct 1, 2026 · TRV-2026-1232

68Index score
179
HealthNewly addedModerate evidence · 1 source

The same CVD classification models lost discriminative performance when deployed across US and Chinese CKD populations, indicating limited cross-population transportability.

Researchers developed interpretable machine learning models to classify cardiovascular disease among middle-aged and elderly CKD patients using US NHANES (n=1057) and China CHARLS (n=1109) cohorts. Seven algorithms were trained and tested internally, with XGBoost achieving test AUC 0.753 in NHANES and logistic regression achieving 0.769 in CHARLS, followed by bidirectional cross-population external validation and SHAP analysis.

Impact 30%
49
Evidence 25%
95
Scale 20%
35
Confidence 15%
87
Recency 10%
98

Updated Sep 30, 2026 · TRV-2026-1220

68Index score
180
HealthNewly addedModerate evidence · 1 source

Current studies show some concerns for bias and low certainty of evidence due to heterogeneity, limited external validation, and variability by target structure and imaging protocol, requiring further multicenter standardized studies before clinical implementation.

This PRISMA-guided systematic review of 30 studies up to December 2025 evaluated machine learning and deep learning algorithms for automated cervical cancer segmentation on MRI, a task central to staging and treatment planning that is currently manual and variable.

Impact 30%
49
Evidence 25%
95
Scale 20%
35
Confidence 15%
87
Recency 10%
98

Updated Sep 30, 2026 · TRV-2026-1219

68Index score

Recomputed live from the record · Oct 11, 2026, 10:25 PM