ReviewCureus2025
Effect of TRIPOD+AI Guidelines on the Reporting Quality of Artificial Intelligence Prediction Models in Orthopaedic Surgery: An 18-Month Bibliometric Study.
Review in Cureus, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
2 citing papers in PubMed.
- Artificial Intelligence in Arthroplasty: A Comprehensive Structured Critical Review and Descriptive Evidence Map of Validation, Uncertainty, and Workflow Integration.Bioengineering (Basel, Switzerland) · 2026Review
- Development and Internal Validation of a LASSO-Based Prediction Model for Colorectal Adenoma Recurrence After Polypectomy.Cancer management and research · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
1 author.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The TRIPOD+AI (Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis plus Artificial Intelligence extension), published in April 2024, provides guidance for transparent reporting of artificial intelligence (AI)-based prediction models. It provides specific guidance for items to include in abstracts in this field. This study evaluated whether reporting quality in orthopaedic AI prediction model abstracts improved following the publication of TRIPOD+AI guidelines. We searched PubMed for English-language studies evaluating AI prediction models in orthopaedics across two 18-month periods: pre-TRIPOD+AI (October 2022 to April 2024) and post-TRIPOD+AI (April 2024 to October 2025). Abstract compliance was assessed against four TRIPOD+AI criteria: performance measure specification (Item 8), sample size and outcome events (Item 9), performance estimates with confidence intervals (Item 11), and study registration (Item 13). Reporting frequencies were compared using chi-squared tests. Among 522 eligible studies (pre-TRIPOD+AI=214, post-TRIPOD+AI=308), reporting of performance measures remained high (96.7% vs 98.4%, p=0.35). Full compliance with Item 9 showed a non-significant increase (32.7% to 39.9%, p=0.11). Reporting of outcome events increased from 36.0% to 44.5% (p=0.06), while participant number reporting declined from 82.2% to 75.0% (p=0.06). Confidence interval reporting remained low (18.7% vs 16.6%, p=0.61), and study registration was nearly absent (0.5% vs 1.0%, p=0.89). No abstract met all four criteria. Eighteen months after its publication, TRIPOD+AI has not measurably improved reporting quality in orthopaedic AI abstracts. Confidence interval reporting and study registration remain particularly deficient. These findings suggest that guideline dissemination alone may be insufficient and that active journal-level implementation strategies may be needed to improve reporting standards.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.