Why this work is in the frame
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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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it