Statistical versus Fuzzy Measures of Variable Interaction in Patients with Stroke
Bibliographic record
Abstract
UNLABELLED: Evidence-based medicine, founded in probability-based statistics, applies what is the case for the collective to the individual patient. An intuitive approach, however, would define structure in the (physiologic) system of interest, the human being, directly relevant to other systems (patients) composed of similar variables. A difference in measure of variable interaction in the patient from that in the collective would show how extrapolation of information from the latter to the single patient is counterintuitive. METHODS: We compare statistical to 'fuzzy' measures of variable interaction. Three diagnostic variables are considered in 30 stroke patients who underwent the same diagnostic tests. 'Fit' (fuzzy information) values [0, 1] for degree of variable severity were expertly assigned by 2 blinded raters for real and fabricated patients. Fabricated patients were composed of real-patient 'fit' values after shuffling. Real and fabricated patients were each numerically represented as a set. Three groups of fabricated patients and the real patient group were studied. Statistical [Pearson's product-moment (regression analysis) and Spearman's rank correlation] and three different fuzzy measures of variable interaction were applied to patient data. RESULTS: Interaction for blood-vessel measured strong in real patients, and weak after one shuffle, using all fuzzy measures. By comparison, the same interaction was found in real patients by only 1 rater (Rater 2) using 1 statistical technique (Spearman's rank correlation) which, as did Pearson product-moment correlation, found a 'significant' interaction between blood-heart in fabricated patients. CONCLUSION: Our study suggests that the measure of variable interaction in nature - as combined in the individual (real) patient - is captured robustly by fuzzy measures and not so by standard statistical measures.
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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 | 0.005 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".