ArticleThe Journal of surgical research2025
Surgeon-Informed Clinical Decision Support Software for Surgical Risk Prediction and Outcomes Tracking.
Article in The Journal of surgical research, 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.
- Genetic Susceptibility to Incisional Hernia Evaluation of Hernia Polygenic Risk Scores.medRxiv : the preprint server for health sciences · 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
12 authors.
Funding
Abstract
introductionThe effective implementation of clinical decision support software (CDSS) requires integration into clinical context and workflow. To prospectively define surgeon-centric design principles, this study employed a modified-Delphi method to guide the development of a novel CDSS app combining incisional hernia risk prediction with patient outcomes tracking.
methodsA three-round, online modified-Delphi panel of 30 surgeons from eight specialties assessed a pilot CDSS app. Feasibility, importance, and acceptability of the application's risk model, user interface, and workflow integration were assessed using Likert scales and qualitative analysis.
resultsSurgeons established clear parameters for model performance (median acceptable false negative rate <10%, false positive rate <15%, minimum area under the receiver operator characteristic curve ≥0.8). Visual risk depictions and transparent weighting of risk factors were highly important for surgeon risk interpretation and patient communication. Team-based data entry, user accounts, data entry and outcome trackers, customizable reminders, and mobile and desktop interfaces were highly important for mitigating user burden, particularly for longitudinal outcomes tracking. Thematic analysis revealed: risk models are primarily used for patients already perceived as high-risk to enhance patient communication, variable statistical literacy impacts surgeon understanding and application of risk model, acceptable model performance metrics vary with clinical context, and minimizing user burden are crucial for successful CDSS adoption.
conclusionsThese findings provide surgeon-informed foundational concepts for developing surgical CDSS tools. For CDSS to be successful, it must function as an interpretable and efficient tool that augments clinical judgment. Addressing end-user needs for model transparency, statistical literacy, and workflow integration are essential for adoption.
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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.