Correlation of Wells Score, Prothrombin Time, Activated Partial Thromboplastin Time, Fibrinogen and D-Dimer Levels with Doppler Ultrasonography in Suspected Deep Vein Thrombosis Patients
Bibliographic record
Abstract
BACKGROUND/AIM: Venous thromboembolism (VTE) occur from formation of blood clots in the veins, which are mostly composed of fibrin and red blood cells with a small component of leukocytes and platelets. Most VTE manifests as deep vein thrombosis (DVT) and pulmonary embolism (PE). The lack of availability of Doppler ultrasound in health facilities especially in remote areas, makes the diagnosis of DVT challenging. There for, history taking, physical examination and laboratory findings are very important in diagnosing DVT especially in those area where Doppler ultrasound unavailable. Based on this we study the correlation Wells scores, Prothrombin Time (PT), Activated Partial Thromboplastin Time (APTT), Fibrinogen, and D-Dimer levels with the findings on Doppler ultrasound in patients with suspected DVT in Wahidin Sudirohusodo Hospital Makassar. METHOD: The study was conducted in Department of Internal Medicine, Wahidin Sudirohusodo Hospital Makassar from 2018 to 2019. Subjects were inpatients in Department of Internal Medicine with DVT suspicion. Wells scores, PT, APTT, Fibrinogen, D-Dimer levels and Doppler ultrasound results of all subjects were recorded and then analyzed. The patient is DVT positive if confirmed by Doppler Ultrasonography. Statistical analysis was performed by descriptive statistical calculations and frequency distribution as well as the Independent-t statistical test, Chi Square test and Fisher Exact test. RESULTS: Among 38 subject, 24 were men (63.2%) and 14 were women (36.8%). We found higher Wells score, shortened PT and APTT, increased fibrinogen in subject with positive Doppler ultrasound, without a significant correlation. A significant correlation was found between increased D-Dimer levels positive Doppler ultrasound results (79.4%, p = 0.048). When Wells score is added with analysis a significant correlation was also found (80.6%, p = 0.044). CONCLUSION: A significant correlation was found between increased D-Dimer levels positive Doppler ultrasound results (79.4%, p = 0.048). When Wells score is added with analysis a significant correlation was also found (80.6%, p = 0.044).
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 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 | 0.002 |
| 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) | 0.002 | 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".