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Record W2323197488 · doi:10.1177/1076029614568712

Effects of Hyperbaric and Decompression Stress on Blood Coagulation and Fibrinolysis

2015· article· en· W2323197488 on OpenAlexafffund
Henry T. Peng, Bruce A. Cameron, Shawn G. Rhind

Bibliographic record

VenueClinical and Applied Thrombosis/Hemostasis · 2015
Typearticle
Languageen
FieldMedicine
TopicTrauma, Hemostasis, Coagulopathy, Resuscitation
Canadian institutionsDefence Research and Development Canada
FundersDefence Research and Development Canada
KeywordsThromboelastometryFibrinolysisThromboelastographyCoagulationThrombelastographyMedicineAnesthesiaDecompressionSurgeryInternal medicine

Abstract

fetched live from OpenAlex

Hyperbaric and decompression stress from diving impairs blood coagulation and fibrinolysis. We hypothesized that thromboelastography (TEG) and rotational thromboelastometry (ROTEM) were suitable to characterize the effects of stress on global hemostatic profiles. We thus conducted a comparative study of the hyperbaric effects on human coagulation using TEG and ROTEM. Maximum clot strength (maximum amplitude [MA]) and clot lysis (lysis index at time 30 minutes [LI30]) were reduced as indicated by TEG MA and EXTEM LI30, respectively. The relative changes in coagulation and fibrinolysis by the hyperbaric effects of diving were indicated by reduced TEG reaction time R at 5 hours, MA at 24 hours postdive, and reduced EXTEM coagulation time at 15 minutes postdive as well as decreased fibrinolysis (EXTEM LI30) at all postdiving time points investigated. Comparison of the parameter values and the diving-induced changes in each parameter between TEG and ROTEM showed both differences and correlations. The discrepancies between the 2 systems may be due to the different assay reagents used. Future studies will seek to further elucidate the changes in blood coagulation and fibrinolysis following varying levels of hyperbaric and decompression stress.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.541
Threshold uncertainty score0.946

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 head0.060
GPT teacher head0.342
Teacher spread0.282 · 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 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

Citations4
Published2015
Admission routes2
Has abstractyes

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