Bibliographic record
Abstract
Imaging is pivotal in chest disease diagnosis as the chest wall renders the heart and lungs inaccessible to physical examination and chest disease symptoms are very nonspecific (eg, cough, shortness of breath). Very early in the development of medical x-ray technology, it was recognized that the chest radiograph could provide essential diagnostic information (eg, pneumonia, lung masses, pneumothorax, cardiac failure). For this reason there was rapid uptake of chest radiography, and it continues to be highly relevant, over 100 years later. However, the chest radiograph has substantial limitations as it only provides a single, 2-dimensional view of the complex 3-dimensional structure of the chest. Even with the addition of the lateral view, detection of small lung nodules may substantially vary amongst expert radiologists. Thus, while lung masses greater than 30 mm in diameter are reliably identified, it has been shown that depending on location, lung nodules may be missed with a median diameter of 19 mm [1]. The strongest prognostic indicator of lung nodule malignant potential is size. Therefore, the detection of small lung cancers is strongly correlated with improved 5-year survival. In the hope of improving lung cancer survival, randomized clinical trials of screening chest radiography were performed in the 1970s. However, these trials failed to show a significant effect on lung cancer mortality [2]. Failure of these trials to impact mortality was believed by some to be secondary to poor imaging of small nodules using plain radiography. Poor imaging also limited our understanding of the link between
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".