From Continuous Predictors to Clinical Thresholds: Early Evidence on Performance Trade-offs of Guideline-Based Categorisation for Ischaemic Stroke Outcome Prediction | SpinGraph: Efficiency framing
SpinGraph analysis of arXiv Artificial Intelligence's From Continuous Predictors to Clinical Thresholds: Early Evidence on Performance Trade-offs of Guideline-…
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From Continuous Predictors to Clinical Thresholds: Early Evidence on Performance Trade-offs of Guideline-Based Categorisation for Ischaemic Stroke Outcome Prediction
stroke outcome prediction, clinical guidelines, categorical encoding, model interpretability
Clinical adoption of stroke outcome ML models is hindered by explanation-clinician reasoning misalignment. The study replaces continuous predictors with guideline-based categorical encodings to improve interpretability. Categorised models match continuous-model performance in 2/3 cohorts and retain consistent global feature importance rankings.
Emphasizes stability of feature hierarchy and statistical non-inferiority in majority cohorts; minimizes the unquantified magnitude and clinical implications of the significant accuracy drop in the third cohort.
Pragmatic clinical translation — prioritizing guideline alignment and clinician reasoning without compromising core model validity.
Guideline-based categorical encoding preserves stroke outcome prediction accuracy and feature importance, making it a viable alternative to continuous inputs.
No reporting of calibration metrics, decision-curve analysis, or clinician usability testing post-deployment; No discussion of how threshold selection may introduce bias across demographic subgroups
The story uses titles, institutions, awards, rankings, partners, experts, or official language to make the subject feel more credible. Watch for loaded terms such as viable design choice, clinically informed, guideline-aligned, statistically indistinguishable. The distribution reads as research announcement. A pressure point: No reporting of calibration metrics, decision-curve analysis, or clinician usability testing post-deployment.
Guideline-based categorisation is thus a viable design choice for stroke-outcome models.
The fully categorised models are statistically indistinguishable from their continuous counterparts in two of the treatment cohorts, with a significant drop in predictive accuracy in one cohort. Global feature importance rankings remain consistent, suggesting that discretising continuous predictors into guideline-based categories preserves the core hierarchy of prognostic factors across all treatment groups.
Extracted statistics from the source narrative
cohorts with statistically indistinguishable performance
Multi-centre European registry stratified by treatment type