Development of a multivariate clinical prediction model for the diagnosis of mild stroke/TIA in physician first-contact patient settings
Bibliographic record
Abstract
Abstract OBJECTIVE To develop a clinical prediction model for diagnosing mild stroke/transient ischemic attack (TIA) in first-contact patient settings. DESIGN: Retrospective study design utilizing logistic regression modeling of patient clinical symptoms collected from patient chart histories and referral data. SETTING: Regional fast-track TIA clinic on Vancouver Island, Canada, accepting referrals from emergency departments (ED) and general practice (GP). PARTICIPANTS: Model development: 4187 ED and GP referred patients from 2008–2011 who were assessed at the TIA clinic. Temporal hold-out validation: 1953 ED and GP referred patients from 2012–2013 assessed at the same clinic. OUTCOMES: Diagnosis of mild stroke/TIA by clinic neurologists. RESULTS: 123 candidate predictors were assessed using univariate feature selection for inclusion in the model, and culminated in the selection of 50 clinical features. Post-hoc investigation of the selected predictors revealed 12 clinically relevant interaction terms. Model performance on the temporal hold-out validation set achieved a sensitivity/specificity of 71.8% / 72.8% using the ROC01 cutpoint (≥ 0.662), and an AUC of 79.9% (95% CI, 77.9%–81.9%). In comparison, the ABCD2 score (≥ 4) achieved a sensitivity/specificity of 70.4% / 54.5% and an AUC of 67.5% (95% CI, 65.2%–69.9%). The logistic regression model demonstrated good calibration on the hold-out set (β 0 = −0.257); β linear = 1.047). CONCLUSIONS The developed diagnostic model performs better than the ABCD2 score at diagnosing mild stroke/TIA on the basis of clinical symptoms. The model has the potential to replace the use of the prognostic ABCD2 score in diagnostic medical contexts in which the ABCD2 score is currently used, such as patient triage.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".