ArticleWorld journal of urology2025
Correspondence regarding "Clinical parameters-based machine learning models for predicting intraoperative hemodynamic instability in hypertensive pheochromocytomas and paragangliomas patients".
Article in World journal of urology, 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
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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.
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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.
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Who cites it
1 citing paper in PubMed.
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1 author.
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Abstract
Zhao et al. present machine-learning models to predict intraoperative hemodynamic instability in hypertensive pheochromocytoma and paraganglioma surgery. The clinical motivation is sound and the reported discrimination and decision-curve metrics indicate promise. Yet the work illustrates two recurring challenges for clinical AI. First, models trained and tested within a single institution risk overestimation without external validation that reflects the intended deployment. Second, calibration and timing of prediction must be explicit to avoid information leakage and to ensure actionable probabilities. Attention to these methodological points will enhance interpretability and safe translation into peri-operative practice.
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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.