Clinical risk scores for predicting stroke-associated pneumonia: A systematic review
Bibliographic record
Abstract
PURPOSE: Several risk stratification scores for predicting stroke-associated pneumonia have been derived. We aimed to evaluate the performance and clinical usefulness of such scores for predicting stroke-associated pneumonia. METHOD: A systematic literature review was undertaken in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses statement, with application of the Quality Assessment of Diagnostic Accuracy-2 tool. Published studies of hospitalised adults with ischaemic stroke, intracerebral haemorrhage, or both, which derived and validated an integer-based clinical risk score, or externally validated an existing score to predict occurrence of stroke-associated pneumonia, were considered and independently screened for inclusion by two reviewers. FINDINGS: We identified nine scores, from eight derivation cohorts. Age was a component of all scores, and the NIHSS score in all except one. Six scores were internally validated and five scores were externally validated. The A2DS2 score (Age, Atrial fibrillation, Dysphagia, Severity [NIHSS], Sex) was the most externally validated in 8 independent cohorts. Performance measures were reported for eight scores. Discrimination tended to be more variable in the external validation cohorts (C statistic 0.67-0.83) than the derivation cohorts (C statistic 0.74-0.85). DISCUSSION: Overall, discrimination and calibration were similar between the different scores. No study evaluated influence on clinical decision making or prognosis. CONCLUSION: The clinical prediction scores varied in their simplicity of use and were comparable in performance. Utility of such scores for preventive intervention trials and in clinical practice remains uncertain and requires further study.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.018 | 0.081 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.014 |
| Bibliometrics | 0.014 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".