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Uso de corticoide na profilaxia para síndrome de embolia gordurosa em pacientes com fratura de osso longo

2013· review· pt· W2005890860 on OpenAlexaff
Douglas Fini Silva, César Vanderlei Carmona, Thiago Rodrigues Araújo Calderan, Gustavo Pereira Fraga, Bartolomeu Nascimento, Sandro Rizoli

Bibliographic record

VenueRevista do Colégio Brasileiro de Cirurgiões · 2013
Typereview
Languagept
FieldMedicine
TopicBone fractures and treatments
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsMedicineJournal clubFat embolismIntensive care unitFat embolism syndromeIntensive care medicineSurgery

Abstract

fetched live from OpenAlex

The "Evidence-based Telemedicine - Trauma & Acute Care Surgery" (EBT-TACS) Journal Club conducted a critical review of the literature and selected three recent studies on the use of corticosteroids for the prophylaxis of fat embolism syndrome. The review focused on the potential role of corticosteroids administration to patients admitted to the intensive care unit (ICU) at risk of developing post-traumatic fat embolism. The first study was prospective and aimed at identifying reliable predictors, which occurred early and were associated with the onset of fat embolism syndrome in trauma patients. The second manuscript was a literature review of the role of corticosteroids as a prophylactic measure for fat embolism syndrome (FES). The last manuscript was a meta-analysis on the potential for corticosteroids to prophylactically reduce the risk of fat embolism syndrome in patients with long bone fractures. The main conclusions and recommendations reached were that traumatized patients should be monitored with non-invasive pulse oximetry and lactate levels since these factors may predict the development of FES, and that there is not enough evidence to recommend the use of steroids for the prophylaxis of this syndrome.

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)0.002
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Scholarly communication, Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesMeta-epidemiology (narrow), Research integrity, Insufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.595
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0030.003
Meta-epidemiology (broad)0.0070.003
Bibliometrics0.0010.002
Science and technology studies0.0010.001
Scholarly communication0.0030.000
Open science0.0020.001
Research integrity0.0030.004
Insufficient payload (model declined to judge)0.0030.002

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 head0.036
GPT teacher head0.329
Teacher spread0.293 · 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
GenreReview

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

Citations5
Published2013
Admission routes1
Has abstractyes

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