Biomolecular characterisation of leucocytes by infrared spectroscopy
Bibliographic record
Abstract
Over the last 15 years, infrared (IR) spectroscopy has developed into a novel and powerful biomedical tool that has multiple applications in the field of haematology. By revealing subtle alterations in both the conformation and concentration of key macromolecules, such as DNA, protein and lipids, IR spectroscopy has been employed to investigate multiple aspects of leucocyte physiology. IR spectroscopy has been used, for example, to diagnose and prognose leukaemia; to characterise differentiation and apoptotic processes; to predict drug sensitivity and resistance in leukaemic patients undergoing chemotherapy; to monitor the response of leucocytes to chemotherapy and to perform human leucocyte antigen matching for bone marrow transplant patients. Such studies have provided insight into pathogenic mechanisms underlying specific leucocyte disorders, especially leukaemia. While it is likely to be some considerable time before IR spectroscopy is sufficiently developed to displace the established technologies, IR spectroscopy has the potential to become a valuable analytic tool in basic and clinical haematology.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.003 |
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".