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Record W2913059726 · doi:10.1161/str.50.suppl_1.tp547

Abstract TP547: Peripheral Blood Indices for Platelet Reactivity and Systemic Inflammation Correlate With Coated-Platelet Trends After Aneurysmal Subarachnoid Hemorrhage

2019· article· en· W2913059726 on OpenAlexaff
Bappaditya Ray, Kimberly Hollabaugh, Lance Ford, George L. Dale, Călin I. Prodan

Bibliographic record

VenueStroke · 2019
Typearticle
Languageen
FieldMedicine
TopicInflammatory Biomarkers in Disease Prognosis
Canadian institutionsKimberly-Clark (Canada)
Fundersnot available
KeywordsMedicineMean platelet volumePlateletInternal medicineGastroenterologySystemic inflammationPlatelet activationSubarachnoid hemorrhageProspective cohort studyNeutrophil to lymphocyte ratioThrombosisFibrinogenLymphocyteImmunologyCardiologyInflammation

Abstract

fetched live from OpenAlex

Introduction: Cerebral micro-thrombosis and neuroinflammation are important mechanisms causing delayed cerebral ischemia (DCI) after aneurysmal subarachnoid hemorrhage (aSAH). Apart from these local events, aSAH also mounts a systemic inflammatory response that may be reflected through various systemic markers. Mean platelet volume to platelet count (MPV:PLT), platelet count to lymphocyte count ratio (PLR), neutrophil count to lymphocyte count ratio (NLR) are established markers for platelet reactivity and systemic inflammation, respectively. Hypothesis: We assessed if peripheral blood indices (PBI) correlated with an established marker for platelet reactivity, coated-platelets, in predicting DCI after aSAH. Methods: A prospective cohort of 44 patients presenting with varying grades of aSAH were enrolled to assess coated-platelet levels in predicting DCI. Non-linear regression analysis was performed to assess the association correlation between coated-platelets and PBI trends in predicting DCI. Results: Twenty-nine (65.9%) of the enrolled patients developed DCI, and a higher rise in coated-platelets from patient’s baseline predicted development of DCI. Using non-linear regression models MPV:PLT showed a rise in ratio during the first 3 days followed by progressive decline. In contrast, PLR and NLR showed initial declines in their respective ratios until 4.2 and 5.8 days respectively followed by a gradual rise. Although slope differences for patients developing DCI as compared to those without DCI for MPV:PLT showed a trend towards significance (p=0.06), intergroup difference for PLR or NLR was not statistically significant. However, a significant relationship between MPV:PLT and coated-platelets (p<0.0001) was observed: with every unit rise in MPV:PLT ratio, coated-platelet levels increased by 0.04%. Similarly, a significant relationship was observed between PLR and coated-platelets (p=0.0001), with every unit rise in PLR, coated-platelets decreased by 2.06%. Conclusion: An early rise in MPV:PLT ratio with corresponding parallel rise in coated-platelet levels allude to presence of prothrombotic factors after aSAH. Also, a fall in PLR and NLR suggest a possible immunosuppressed state during the early phase after aSAH.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.001

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.

Opus teacher head0.006
GPT teacher head0.222
Teacher spread0.216 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

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Citations1
Published2019
Admission routes1
Has abstractyes

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