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Clinic Shortlists and Sources Across ChatGPT Models and Reasoning Efforts
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An AI research paper on Clinic Shortlists and Sources Across ChatGPT Models and Reasoning Efforts.
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Chinese explanation / 中文解读
中文解读待补充：本站会优先为大语言模型、生成式AI、ChatGPT相关技术、计算机视觉、深度学习等高价值论文补充中文说明。
Original abstract
Clinic Shortlists and Sources Across ChatGPT Models and Reasoning Efforts is a descriptive stability benchmark built from 450 core answers: three exact NYC commercial prompts across five ChatGPT model/reasoning configurations, with 30 isolated executions per cell. Fifteen technical preflight answers are excluded from all reported denominators. Headline within-configuration provider-set overlap was 80.3% for IVF, 34.7% for full-arch dental implants, and 53.9% for bariatric programs; corresponding visible-domain overlap was 51.1%, 28.3%, and 44.8%. The visible-source core contains 3,757 markdown-link occurrences, 3,690 unique answer-by-URL pairs, and 3,194 answer-by-domain pairs. Collection used Codex CLI 0.147.0 authenticated through one ChatGPT login with live search. It did not use the consumer ChatGPT Free/Plus UI or API-key billing. Position means presentation order, not quality ranking. Reader-visible domains do not establish causal recommendation sources. Physical US IP/location was not controlled. Boris Teplyakov reviewed the methodology as SEO Lead. This was not clinical review, peer review, a formal external audit, or an assessment of named providers. Article and full methodological context: https://rotgar.com/medical/resources/chatgpt-clinic-shortlists-model-effort-stability
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