Diabetes does not influence activation of coagulation, fibrinolysis or anticoagulant pathways in Gram-negative sepsis (melioidosis)
Bibliographic record
Abstract
Diabetes is associated with a disturbance of the haemostatic balance and is an important risk factor for sepsis, but the influence of diabetes on the pathogenesis of sepsis remains unclear. Melioidosis ( Burkholderia pseudomallei infection) is a common cause of community-acquired sepsis in Southeast Asia and northern Australia. We sought to investigate the impact of pre-existing diabetes on the coagulation and fibrinolytic systems during sepsis caused by B.pseudomallei . We recruited a cohort of 44 patients (34 with diabetes and 10 without diabetes) with culture-proven melioidosis. Diabetes was defined as a pre-admission diagnosis of diabetes or an HbA₁c>7.8% at enrolment. Thirty healthy blood donors and 52 otherwise healthy diabetes patients served as controls. Citrated plasma was collected from all subjects; additionally in melioidosis patients follow-up specimens were collected seven and ≥ 28 days after enrolment where possible. Relative to uninfected healthy controls, diabetes per se (i.e. in the absence of infection) was characterised by a procoagulant effect. Melioidosis was associated with activation of coagulation (thrombin-antithrombin complexes (TAT), prothrombin fragment F₁+₂ and fibrinogen concentrations were elevated; PT and PTT prolonged), suppression of anti-coagulation (antithrombin, protein C, total and free protein S levels were depressed) and abnormalities of fibrinolysis (D-dimer and plasmin-antiplasmin complex [PAP] were elevated). Remarkably, none of these haemostatic alterations were influenced by pre-existing diabetes. In conclusion, although diabetes is associated with multiple abnormalities of coagulation, anticoagulation and fibrinolysis, these changes are not detectable when superimposed on the background of larger abnormalities attributable to B. pseudomallei sepsis.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".