Imaging conundrum – COVID-related lung changes or connective tissue disease-related interstitial lung disease
Bibliographic record
Abstract
ENWEndNote BIBJabRef, Mendeley RISPapers, Reference Manager, RefWorks, Zotero AMA Vyas S, S. Bhalla A. Imaging conundrum – COVID-related lung changes or connective tissue disease-related interstitial lung disease. Polish Journal of Radiology. 2021;86(1):157-158. doi:10.5114/pjr.2021.104620. APA Vyas, S., & S. Bhalla, A. (2021). Imaging conundrum – COVID-related lung changes or connective tissue disease-related interstitial lung disease. Polish Journal of Radiology, 86(1), 157-158. https://doi.org/10.5114/pjr.2021.104620 Chicago Vyas, Surabhi, and Ashu S. Bhalla. 2021. "Imaging conundrum – COVID-related lung changes or connective tissue disease-related interstitial lung disease". Polish Journal of Radiology 86 (1): 157-158. doi:10.5114/pjr.2021.104620. Harvard Vyas, S., and S. Bhalla, A. (2021). Imaging conundrum – COVID-related lung changes or connective tissue disease-related interstitial lung disease. Polish Journal of Radiology, 86(1), pp.157-158. https://doi.org/10.5114/pjr.2021.104620 MLA Vyas, Surabhi et al. "Imaging conundrum – COVID-related lung changes or connective tissue disease-related interstitial lung disease." Polish Journal of Radiology, vol. 86, no. 1, 2021, pp. 157-158. doi:10.5114/pjr.2021.104620. Vancouver Vyas S, S. Bhalla A. Imaging conundrum – COVID-related lung changes or connective tissue disease-related interstitial lung disease. Polish Journal of Radiology. 2021;86(1):157-158. doi:10.5114/pjr.2021.104620.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.187 | 0.156 |
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".