ArticleOTO open
Utility and Safety of Artificial Intelligence for Patient-Initiated Contact After Functional Rhinoplasty.
Article in OTO open. 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.
- Large language models for post-discharge follow-up in erectile dysfunction care: a narrative review.Translational andrology and urology · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Objective: To understand the content of patient calls after functional rhinoplasty and to evaluate artificial intelligence chatbots' ability to provide accurate, intelligible, and safe responses. Study Design: Retrospective review. Setting: Tertiary-care institution. Methods: A single-institution, retrospective review was conducted for patients who underwent functional rhinoplasty between 2017 to 2023. Postoperative calls and messages prior to first follow-up were analyzed, and 48 representative prompts were generated, including 6 "Red Flag Questions" indicating potential complications. Prompts were input into 4 chatbots (ChatGPT, Claude, Perplexity, and Gemini). Two independent, blinded experts graded responses using a Likert-style Global Quality Scale (GQS) and a binary Expert Opinion Question (EOQ; "Would you feel comfortable if your patient received this response rather than speaking with your staff?"). Flesch-Kincaid (FK) Grade Levels measured readability. Chi-square and Mann-Whitney Results: Of 378 patients, 137 (36%) initiated contact, with 181 total calls. Common concerns included pain (19%) and medication questions (14%). Seventy-three percent (n = 132) received routine counseling, with no complications at first follow-up. ChatGPT produced "Good" or "Excellent" responses 98% of the time, significantly outperforming the next-best chatbot (Perplexity, 79%; Conclusion: Patient calls are common after functional rhinoplasty. Most can be managed with reassurance. Chatbots, especially ChatGPT, provide reliable responses, which may improve satisfaction and reduce workload without compromising safety. Future development should focus on readability.
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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.