<title>Disease pattern recognition in FT-IR spectra of human sera</title>
Bibliographic record
Abstract
We observed differences between the mid-infrared spectra of sera originating from healthy volunteers and from patients with diabetes mellitus or rheumatoid arthritis. These differences were found to be significant in terms of the Fisher criterion, the t-test, and the Kolmogorov-Smirnov test. The significance allows for a classification of the spectra and a probability (`DPR-score') of belonging to the class `healthy' can be computed. In comparing the samples from 80 diabetes patients with samples from 40 healthy volunteers we are able to achieve a sensitivity and a specificity of 80% and above. The DPR-score correlates better with the actual status of health than the glucose concentration alone. In a study on rheumatoid arthritis we compared the spectral signatures of sera taken from 188 rheumatoid arthritis patients and sera from 196 healthy volunteers. By applying linear discriminant analysis to 2/3 of the samples we are able to classify the remaining third of the samples (independent validation) with a sensitivity of 84% and a specificity of 88%.
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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 | 0.001 | 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) | 0.028 | 0.009 |
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".