Platelet Indices as a Marker of Severity in Non-diabetic NonHypertensive Acute Ischemic Stroke Patients.
Bibliographic record
Abstract
BACKGROUND: Platelet activation & aggregation are critical in pathogenesis of acute ischemic stroke. Mean platelet volume (MPV) & Platelet distribution width (PDW) are markers & determinants of platelet function. Larger platelets are metabolically more active, produce more prothrombotic factors, aggregate more easily & act as index of homeostasis and its dysfunction thrombosis. MATERIAL: We studied 70 non diabetic non hypertensive ischemic stroke patients without previous thrombotic events & not on anti platelet medications within 24 hour of onset of symptoms & compared with equal number of age and sex matched controls. Severity of stroke was calculated by Canadian neurological scale (CNS).Platelet indices were obtained from SYSMEX KX-21. OBSERVATION: Mean age of patients was 55 ± 7.11 and of controls was 52 ± 5.37. According to CNS patients were divided in two groups; with comprehension deficit (1st group, 32 patients) & without comprehension deficit (2nd group, 38 patients).Mean value for PDW & MPV in 1st group was 18.675 ± 3.494 & 12.894 ± 1.270 respectively and in 2nd group was 18.62 ± 3.387 & 12.42 ± 0.984 respectively and was significantly higher than mean value of 15.694 ± 3.127 & 10.46 ± 1.273 of PDW & MPV respectively in controls. In both study groups PDW & MPV was found to be significantly associated with severity of motor deficit. CONCLUSION: In patients of ischemic stroke platelet indices may be used for predicting severity of motor deficit. Although larger sample size and multivariate analysis is required before this can be used regularly in clinical practice.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".