Pattern of platelet indices in hypertension: a single-centre experience for a primary care setting
Bibliographic record
Abstract
Search, G -Funds CollectionBackground.As a neglected piece of cell blood count, platelet indices are readily available in a primary care setting.Current literature aimed at the role of platelet indices in hypertensive disorders is focused mainly on a single platelet index.Objectives.To better clarify alterations in the pattern of platelet indices in hypertensive disorders, we investigated the relation between a set of platelet indices and hypertensive disorders defined by ambulatory blood pressure monitoring (ABPM).Material and methods.This cross-sectional study was conducted on 283 patients referred to the Hypertension Clinic of our hospital.ABPM was performed for all cases, and patients were accordingly classified as hypertensive (61.8%) and non-hypertensive (38.2%), as well as dipper (60%) and non-dipper (40%).Blood samples were collected for cell blood count, and ensuing platelet indices were compared between these groups.Results.The mean level of plateletcrit (PCT) was significantly higher in hypertensive subjects than non-hypertensive individuals (0.235% versus 0.251%, p = 0.03).The difference of mean platelet volume (MPV), platelet distribution width (PDW) and platelet large cell ratio (PLCR) was not significant between hypertensive and non-hypertensive cases.The levels of platelet indices were not significantly different between dipper and non-dipper individuals.The mean MPV and PLCR was significantly higher in hypertensive patients with coexisting diabetes mellitus.Conclusions.We identified a different pattern of platelet indices as "elevated plateletcrit and normal other platelet volume indices" in hypertensive patients.Considering the patterns of alteration in platelet indices, it may be better to describe their role as a biomarker in hypertensive disorders.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".