Prognostic factors of first-ever stroke patients in suburban Malaysia by comparing regression models
Bibliographic record
Abstract
Introduction: The aim of this study was to compare regression models based on the parameter estimates of prognostic factors of mortality in first-ever stroke patients. Methods: A retrospective study among 432 first-ever stroke patients admitted to Hospital Universiti Sains Malaysia, Kelantan, Malaysia, was carried out. Patient’s medical records were extracted using a standardized data collection sheet. The statistical analyses used for modelling the prognostic factors of mortality were Cox proportional hazards regression, multinomial logistic regression, and multiple logistic regression. Results: A total of 101 (23.4%) events of death were identified and 331 patients (76.6%) were alive. Despite using three different statistical analyses, the results were very similar in terms of five major aspects of parameter estimates, namely direction, estimation, precision, significance, and magnitude of risk assessment. It was reported slightly better in Cox proportional hazards regression model, especially in terms of the precision of the results. Conclusions: Given that this study had compared the findings from three different types of advanced statistical methods, this research has clearly yielded that with data of high quality, the selection of appropriate statistical method should not be a worrisome problem for researchers who may not be of expertise in the field of medical statistics.
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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 | 0.012 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".