ArticleCureus2025
Decoding Mohs Micrographic Surgery Through an Artificial Intelligence-Powered Search Analytics Approach.
Article in Cureus, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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
1 citing paper in PubMed.
- Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background Patients often seek information online regarding Mohs micrographic surgery. Google's "People Also Ask" algorithm provides insights into patients' questions, compiling frequent queries through machine learning. The experimenters aimed to utilize modern search analytics methods with the integration of machine learning qualitative data analysis to determine patient concerns and questions surrounding Mohs surgery. Methodology Rothwell's classification system and the Journal of the American Medical Association benchmark criteria were used to evaluate question-website pairs generated by Google's "People Also Ask" function. Statistical analyses assessed machine learning classification performance and relationships between search terms, question types, and website categories. Results The highest proportion of questions belonged to technical details (19.8%), follow-up care (12.5%), and indications (11.6%). Among website types, government sites had the highest Journal of the American Medical Association benchmark scores. Compared with human reviewers, machine learning algorithms exhibited kappa values as high as 0.86. Conclusions Mohs surgery patients are interested in understanding the technical details of their condition and procedure. Healthcare professionals should address these concerns to ensure greater compliance with treatment protocol and mitigate the risk of iatrogenic trauma. In qualitative research applications involving a panel of reviewers, high-performing large language models offer a suitable component of a review board and complement human expertise.
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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.