Bibliographic record
Abstract
In this paper, we study the reliability of X-ray imaging diagnosis considering human cognitive abilities (e.g., spatial orientation, visualization, line orientation, and perceptual speed), which play a vital role in the clinical decision making that requires classification systems. Also, this study explores sex influence on X-ray imaging diagnosis based on 176 X-ray images evaluated by 10 female radiologists and 8 male radiologists. Most related literature focuses on a binary classification (True or False) that uses a set of features derived from a given pattern. Also, they utilize the Receiver Operating Characteristics (ROC) analyses for assessing the accuracy of X-ray diagnosis. In this study, we use fuzzy benchmarking to construct fuzzy classification systems required for fuzzy medical decision-making and fuzzy reliability assessment. The proposed method differentiates the influence of human cognitive abilities and sex in X-ray diagnosis. The results from this study shows reliability of X-ray diagnosis is high and male radiologists excel in spatial and line orientation and female radiologists perform better in perceptual speed while both are competent in visualization ability.
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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 | 0.003 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 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".