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Profilaxia de tromboembolismo venoso em pacientes com lesão cerebral traumática

2012· article· pt· W1973218512 on OpenAlexaff
Tanya L. Zakrison, Bruno M. Pereira, Antonio Marttos, Gustavo Pereira Fraga, Bartolomeu Nascimento, Sandro Rizoli

Bibliographic record

VenueRevista do Colégio Brasileiro de Cirurgiões · 2012
Typearticle
Languagept
FieldMedicine
TopicVenous Thromboembolism Diagnosis and Management
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsMedicineRandomized controlled trialCritical appraisalHematomaTraumatic brain injuryLow molecular weight heparinTrauma centerVenous thromboembolismIntensive care medicineRetrospective cohort studySurgeryHeparinThrombosis

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Scholarly communication, Insufficient payload (model declined to judge)
Consensus categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.074
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.025
GPT teacher head0.294
Teacher spread0.268 · 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; both teacher heads agree on what is shown here.

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".

Quick stats

Citations6
Published2012
Admission routes1
Has abstractyes

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