Profilaxia de tromboembolismo venoso em pacientes com lesão cerebral traumática
Bibliographic record
Abstract
Traumatic brain injury (TBI) with associated intracranial hemorrhage (ICH) occurs frequently in trauma. Trauma patients are also at high risk of developing venous thromboembolic (VTE) complications. Low Molecular Weight Heparin (LMWH) is used in trauma patients as prophylaxis to reduce the risk of VTE events. It remains unclear, however, if LMWH is safe to use in trauma patients with ICH for fear of hematoma progression. The "Evidence-based telemedicine: trauma & acute care surgery (EBT-TACS)" Journal Club performed a critical appraisal of 3 recent and most relevant studies on timing to initiate, safety and use of LMWH in trauma patients with ICH. Specifically, we appraised a i) critical literature review on the topic, ii) a multicenter, retrospective cohort study assessing the safety of LMWH in trauma patients with ICH and iii) a randomized, pilot study assessing the feasibility and event rates of ICH progression, laying the groundwork for future randomized controlled trials (RCT) on the topic. Some results are conflicting, with the highest level of evidence being the pilot RCT demonstrating the safety for early use of LMWH in TBI with ICH. Much of this research, however, was generated by a single center and consequently lacks external validity. Furthermore, clinical recommendations cannot be generated based on pilot studies. Evidence-based guidelines and recommendations could not be made at this time, until the completion of further studies on this challenging topic.
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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 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".