Assessment of Protein C Levels in Patients with Ischaemic Stroke in South-South Nigeria: A Study of Cases in University of Benin Teaching Hospital, Benin City
Bibliographic record
Abstract
Background/Objective: Protein C (PC) is a vitamin K – dependent coagulation inhibitor produced in the liver. Acting together with its cofactor, protein S (PS), activated PC inhibits activated factors V and VIII thus downregulating thrombin generation which may predispose to inappropriate clot formation. This study aimed to ascertain the role of protein C deficiency in the development of ischaemic stroke in order to establish its relevance in stroke management in our environment. Materials and Methods: Sixty-five ischaemic stroke patients and controls matched for age and sex were recruited in the study, blood samples were taken for haematological indices, prothrombin and activated partial thromboplastin times (PT and APTT) and protein C. Functional and qualitative assessments of protein C were done by chromogenic and immunoassay methods respectively. Data were analyzed with SPSS version 18. Results: A total of 130 subjects comprising 65 stroke subjects and 65 controls were recruited in the study. Mean age of the stroke group was 60.4±12.3yrs and the control is 59.0±14.1yrs. The mean difference in PC Ag level, PC Ag(%) and functional activity between the groups were not statistically significant (p<0.05). Total WBC count in the stroke subjects was significantly higher than the controls (p=0.001). The platelet count was also higher and haemoglobin concentrations lower in stroke patients though not statistically significant. The prothrombin and activated partial thromboplastin times (APTT) in test and control groups are not significant. Conclusion: This study showed that protein C may not play a significant role in the development of ischaemic stroke in our population.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".