MétaCan
Menu
Back to cohort
Record W2564229019 · doi:10.14740/jnr.v6i5-6.405

Fat Embolism Syndrome: Case Report

2016· article· en· W2564229019 on OpenAlexvenueno aff
Sreenivasa Rao Sudulagunta, Monica Kumbhat, Mahesh Babu Sodalagunta, Aravinda Settikere Nataraju, Mona Sepehrar, Shiva Kumar Bangalore Raja

Bibliographic record

VenueJournal of Neurology Research · 2016
Typearticle
Languageen
FieldMedicine
TopicBone fractures and treatments
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineFat embolismFat embolism syndromeAsymptomaticSurgeryEmbolismIncidence (geometry)

Abstract

fetched live from OpenAlex

Fat embolism syndrome (FES) is a systemic inflammatory cascade affecting multiple organ systems occurring after trauma, orthopedic procedures and rarely in non-traumatic patients causing high morbidity and mortality. Fat emboli develop in many patients with bone fractures (incidence of this problem can be up to 90% in patients who have sustained major injuries), but are usually asymptomatic. A minority of patients develop signs and symptoms of organ system dysfunction due to mechanical obstruction of capillaries by fat emboli or due to fat hydrolyzing to fatty acids. A triad of lung, brain and skin involvement develops after 24 - 72 hours of asymptomatic period. This symptom complex is known as FES. Fat embolism is diagnosed clinically with non-specific and insensitive diagnostic tests. Treatment of FES is supportive and in most cases can be prevented by early fixation of large bone fractures. Here we report a case of traumatic fat embolism, treated successfully with supportive management. Diagnosis of FES needs high index of suspicion and use of clinical criteria along with imaging. J Neurol Res. 2016;6(5-6):114-117 doi: https://doi.org/10.14740/jnr405w

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Case report · Consensus signal: Case report
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.056
Threshold uncertainty score0.336

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.080
GPT teacher head0.419
Teacher spread0.339 · 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; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designCase report
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

Citations1
Published2016
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Neurology ResearchSame topicBone fractures and treatmentsFrench-language works237,207