Construction and validation of risk prediction model of dysarthria in patients with stroke
Bibliographic record
Abstract
ObjectiveTo explore the risk prediction model of dysarthria in patients with stroke，construct risk prediction model and validate their effectiveness.MethodsConvenience sampling was used to select stroke patients from 4 tertiary hospitals in Hebei province from July to November 2022.Patients in case group were 173 cases and control group were 316 cases.A general information questionnaire，oral feature questionnaire，Chinese version of Brief Oral Health Status Examination，National Institutes of Health Stroke Scale，and the Chinese version of Montreal Cognitive Assessment Scale were used to survey patients，and Lovett muscle strength grading and water swallow test were used to evaluate patients.ResultsAge≥60 years old，brainstem stroke，history of stroke，poor tongue and body movement，moderate and severe neurological dysfunction，poor oral health，cognitive and swallowing dysfunction were risk factors of dysarthria in patients with stroke（P<0.05）. The area under the curve of receiver operator characteristic of predictive model based on risk factors was 0.837［95%CI（0.798，0.876）］，the sensitivity was 0.905，the specificity was 0.642，Brier score calculated by Bootstrap internal validation was 0.148，H⁃L test showed P=0.177，the decision curve showed high risk probability ranged from 0.08 to 0.10，and the C⁃index of the Nomogram was 0.837，P=0.743.ConclusionsMedical staff should focus on the age，stroke history，stroke location，degree of neurological dysfunction，tongue and oral health，cognitive and swallowing functions of stroke patients.The predictive model constructed in this study can be used to evaluate patients and develop targeted measures early.
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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 | 0.011 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| 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".