Novel Clinical Prediction Model: Integrating A2DS2score with 24-hour ASPECTS and Red Cell Distribution Width for EnhancedPrediction of Stroke-Associated Pneumonia following Intravenous Thrombolysis v1
Bibliographic record
Abstract
Background:Stroke-associated pneumonia (SAP) is a common leading cause of death during the acute phase. The A2DS2 score has been widely used to predict the risk of SAP. However, 24-hour non-contrast computed tomography-Alberta Stroke Program Early CT Score (NCCT-ASPECTS) and red cell distribution width (RDW) were not included in this scale. The purpose of the present study was to investigate the prognostic added value of combining 24-hour NCCT-ASPECTS and RDW with the A2DS2 score. Methods:A retrospective study of thrombolyzed acute ischemic stroke (AIS) patients from January 2015 to July 2022. Data on A2DS2 scores, 24-hour NCCT-ASPECTS, and RDW were collected. Three logistic regression models were created: Model A used only the traditional A2DS2 score; Model B (A2DS2-c) calculated probabilities using a logistic equation; and Model C (combined A2DS2-MFP) used multivariable fractional polynomial logistic regression and incorporated the A2DS2 score, 24-hour NCCT-ASPECTS, and RDW. Ischemic brain lesions in the middle cerebral artery area were assessed using 24-hour NCCT-ASPECTS after completing 24-hour intravenous thrombolysis. Results:Among a cohort of 345 thrombolyzed AIS patients, 70 individuals (20.3%) experienced SAP. The area under the receiver operating characteristic (AuROC) of 24-hour NCCT-ASPECTS and RDW were 0.841 and 0.621, respectively. The combined A2DS2-MFP calculation was significantly superior to the traditional A2DS2 score and A2DS2-c calculation (AuROC 0.917 vs. 0.880, P=0.026, and 0.917 vs. 0.888, P=0.024). Conclusion: This study found that the 24-hour NCCT-ASPECTS and RDW enhanced the predictive value of the A2DS2 score for SAP after IV-tPA. The combined A2DS2-MFP model performed excellently in predictive performance, offering robust early SAP detection and potentially improving patient survival. Implementing this novel model in resource-constrained clinical settings could aid clinicians in effective monitoring, enabling risk stratification to guide clinical management.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".