Bibliographic record
Abstract
Sepsis, an innate immunological response of systemic inflammation to infection, is a growing problem worldwide with a relatively high mortality rate. Immediate treatment is required, necessitating quick, early and accurate diagnosis. Rapid molecular-based tests have been developed to address this need, but still suffer some disadvantages. The most commonly studied biomarkers of sepsis are reviewed for their current uses and diagnostic accuracies, including C-reactive protein, procalcitonin, serum amyloid A, mannan and IFN-γ-inducible protein 10, as well as other potentially useful biomarkers. A singular ideal biomarker has not yet been identified; an alternative approach is to shift research focus to determine the diagnostic relevancy of multiple biomarkers when used in concert. Challenges facing biomarker research, including lack of methodology standardization and assays with better detection limits, are discussed. The ongoing efforts in the development of a multiplex point-of-care testing kit, enabling quick and reliable detection of serum biomarkers, may have great potential for early diagnosis of sepsis.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.003 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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 teacher head, 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".