Vibrational spectroscopic analysis of blood for diagnosis of infections and sepsis: a review of requirements for a rapid diagnostic test
Bibliographic record
Abstract
Infections and sepsis represent a growing global burden. There is a widespread clinical need for a rapid, high-throughput and sensitive technique for the diagnosis of infections and detection of invading pathogens and the presence of sepsis. Current diagnostic methods primarily consist of laboratory-based haematology, biochemistry and microbiology that are time consuming, labour- and resource-intensive, and prone to both false positive and false negative results. Current methods are insufficient for the increasing demands on healthcare systems, causing delays in diagnosis and initiation of treatment, due to the intrinsic time delay in sample preparation, measurement, and analysis. Vibrational spectroscopic techniques can overcome these limitations by providing a rapid, label-free and low-cost method for blood analysis, with limited sample preparation required, potentially revolutionising clinical diagnostics by producing actionable results that enable early diagnosis, leading to improved patient outcomes. This review will discuss the challenges associated with the diagnosis of infections and sepsis, primarily within the UK healthcare system. We will consider the clinical potential of spectroscopic point-of-care technologies to enable blood analysis in the primary-care setting.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".