Burn Patients with TBSA ≤30% Display Whole Blood Hypercoagulablility 14 days’ Post Trauma
Bibliographic record
Abstract
Introduction: The aim of this study was to investigate baseline and 14 days prospective changes in viscoelastic whole blood coagulation by thromboelastography (TEG) in patients with moderate and severe burns corresponding to ≥ 10 % total body surface area (TBSA). Methods: 13 patients with burns ≥ 10% were included in the study. TEG analysis and standard coagulation parameters as APTT, INR, platelet count, and fibrinogen were carried out at admission and the following 14 days. Results: Most TEG variables changed in a hypercoagulable direction the first 2 weeks following burn injury. a values increased (p<0.0004) from post burn day one 54 degrees to 69 degrees’ day seven. MA increased (p<0.05) from 62 mm at day one to 73 mm at day five. K values decreased (p<0.03) from 2.9 minutes at day one to 1.9 minutes at post burn day three. MTG (p<0.005) started at 15994 mm*100/sec at day one and increased to 30549 mm*100/sec at post burn day eight. All remained significant until day 14. R, Ly-30 and TMG did not change in the observation period. In contrast APTT increased (p<0.05) from 25.8 sec. to 30.2 sec. in six days. Fibrinogen increased from 11.55mmol/L to 20.37 mmol/L day six (p<0.05) and INR decreased from 1.17 to 1.05 day five (p< 0.02). Conclusion: Burn patients with TBSA ≤ 30% and who are not subject to surgical intervention are in a hypercoagulable state detected by whole blood viscoelastic haemostatic assays TEG that extended 2 weeks post injury
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".