Bilateral Normal Tension Glaucoma in a Healthy Child Without Myopia
Bibliographic record
Abstract
Department of Ophthalmology, Seoul National University College of Medicine, Seoul National University Bundang Hospital, Seongnam, Republic of Korea Funding This work was supported by the Seoul National University Bundang Hospital Research Fund (grant number: 02-2017-0037) and a grant of Patient-Centered Clinical Research Coordinating Center funded by the Ministry of Health & Welfare, Republic of Korea (grant numbers: HI19C0481, HC19C0276). The funders had no role in the design or conduct of this research. Availability of data and material Not applicable. Code availability Not applicable Authors' contributions Dong Kyun Han: manuscript preparation; Eun Ji Lee: concepts, design, manuscript editing, and manuscript review; Tae-Woo Kim: manuscript review. Ethics approval This study was approved by the Institutional Review Board of Seoul National University Bundang Hospital (IRB No. B-2305-830-701) Consent to participate Not applicable. Conflicts of interests None of the authors have proprietary or commercial interest in any of the materials discussed in this article. Reprints: Eun Ji Lee, MD, Department of Ophthalmology, Seoul National University Bundang Hospital 82, Gumi-ro 173 beon-gil, Bundang-gu, Seongnam-si, Gyeonggi-do, Republic of Korea. Tel: 82-31-787-7378, Fax: 82-31-787-4057 (e-mail: [email protected]).
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".