MétaCan
Menu
Back to cohort
Record W2509384310 · doi:10.1155/2016/4206397

Compartment Syndrome as a Result of Systemic Capillary Leak Syndrome

2016· article· en· W2509384310 on OpenAlexaff
Kwadwo Kyeremanteng, Gianni D’Egidio, Cynthia Wan, Alan D. Baxter, Hans Rosenberg

Bibliographic record

VenueCase Reports in Critical Care · 2016
Typearticle
Languageen
FieldMedicine
TopicMuscle and Compartmental Disorders
Canadian institutionsOttawa HospitalUniversity of Ottawa
Fundersnot available
KeywordsCompartment (ship)LeakMedicineEngineeringHistory

Abstract

fetched live from OpenAlex

Objective. To describe a single case of Systemic Capillary Leak Syndrome (SCLS) with a rare complication of compartment syndrome. Patient. Our patient is a 57-year-old male, referred to our hospital due to polycythemia (hemoglobin (Hgb) of 220 g/L), hypotension, acute renal failure, and bilateral calf pain. Measurements and Main Results. The patient required bilateral forearm, thigh, and calf fasciotomies during his ICU stay and continuous renal replacement therapy was instituted following onset of acute renal failure and oliguria. Ongoing hemodynamic (Norepinephrine and Milrinone infusion) and respiratory (ventilator) support in the ICU was provided until resolution of intravascular fluid extravasation. Conclusions. SCLS is an extremely rare disorder characterized by unexplained episodic capillary hyperpermeability, which causes shift of volume and protein from the intravascular space to the interstitial space. Patients present with significant hypotension, hemoconcentration, hypovolemia, and oliguria. Severe edema results from leakage of fluid and proteins into tissue. The most important part of treatment is maintaining stable hemodynamics, ruling out other causes of shock and diligent monitoring for complications. Awareness of the clinical syndrome with the rare complication of compartment syndrome may help guide investigations and diagnoses of these critically ill patients.

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)0.000
metaresearch head score (Gemma)0.000
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 score0.015
Threshold uncertainty score0.585

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.037
GPT teacher head0.348
Teacher spread0.311 · 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

Citations13
Published2016
Admission routes1
Has abstractyes

Explore more

Same venueCase Reports in Critical CareSame topicMuscle and Compartmental DisordersFrench-language works237,207