The role of platelets in sepsis
Bibliographic record
Abstract
Platelets are small circulating anucleate cells that are of crucial importance in haemostasis. Over the last decade, it has become increasingly clear that platelets play an important role in inflammation and can influence both innate and adaptive immunity. Sepsis is a potentially lethal condition caused by detrimental host response to an invading pathogen. Dysbalanced immune response and activation of the coagulation system during sepsis are fundamental events leading to sepsis complications and organ failure. Platelets, being major effector cells in both haemostasis and inflammation, are involved in sepsis pathogenesis and contribute to sepsis complications. Platelets catalyse the development of hyperinflammation, disseminated intravascular coagulation and microthrombosis, and subsequently contribute to multiple organ failure. Inappropriate accumulation and activity of platelets are key events in the development of sepsis-related complications such as acute lung injury and acute kidney injury. Platelet activation readouts could serve as biomarkers for early sepsis recognition; inhibition of platelets in septic patients seems like an important target for immune-modulating therapy and appears promising based on animal models and retrospective human studies.
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 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".