Is antiplatelet treatment effective at attenuating the progression of white matter hyperintensities?
Bibliographic record
Abstract
OBJECTIVE: We performed this study to assess the effect of an antiplatelet agent on the progression of white matter hyperintensities (WMH). METHODS: From August 2003 to May 2005, we consecutively enrolled patients who underwent brain magnetic resonance imaging (MRI) for health check-up purposes and showed no significant findings other than WMH of any degree. Patients were divided into two groups based on whether or not they received antiplatelet therapy. All patients had a follow-up brain MRI after 5 years and WMH volume change was measured using imaging analysis software. To minimize selection bias potentially arising from antiplatelet treatment assignment, analyses were inverse probability weighted. RESULTS: Among the 93 patients who met the inclusion criteria, 54 patients (58.1%) were grouped as the antiplatelet group (AG), and the remaining 39 patients (41.9%) as the non-antiplatelet group (NAG). After inverse propensity weighting, all baseline characteristics were similar between the two groups, and antiplatelet treatment did not show any significant effect on the total WMH volume change (p = 0.957). CONCLUSION: Antiplatelet medication may not alter the progression of WMH.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".