Cardiovascular risk in patients with alopecia areata:a cross-sectional study
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
AMA Waśkiel-Burnat A, Dorobek W, Kotowska M, et al. Cardiovascular risk in patients with alopecia areata: a cross-sectional study. Dermatology Review/Przegląd Dermatologiczny. 2023;110(2):133-141. doi:10.5114/dr.2023.128016. APA Waśkiel-Burnat, A., Dorobek, W., Kotowska, M., Starace, M., Piraccini, B., & Olszewska, M. et al. (2023). Cardiovascular risk in patients with alopecia areata: a cross-sectional study. Dermatology Review/Przegląd Dermatologiczny, 110(2), 133-141. https://doi.org/10.5114/dr.2023.128016 Chicago Waśkiel-Burnat, Anna, Wioleta Dorobek, Maja Kotowska, Michela Starace, Bianca Maria Piraccini, Małgorzata Olszewska, and Lidia Rudnicka. 2023. "Cardiovascular risk in patients with alopecia areata: a cross-sectional study". Dermatology Review/Przegląd Dermatologiczny 110 (2): 133-141. doi:10.5114/dr.2023.128016. Harvard Waśkiel-Burnat, A., Dorobek, W., Kotowska, M., Starace, M., Piraccini, B., Olszewska, M., and Rudnicka, L. (2023). Cardiovascular risk in patients with alopecia areata: a cross-sectional study. Dermatology Review/Przegląd Dermatologiczny, 110(2), pp.133-141. https://doi.org/10.5114/dr.2023.128016 MLA Waśkiel-Burnat, Anna et al. "Cardiovascular risk in patients with alopecia areata: a cross-sectional study." Dermatology Review/Przegląd Dermatologiczny, vol. 110, no. 2, 2023, pp. 133-141. doi:10.5114/dr.2023.128016. Vancouver Waśkiel-Burnat A, Dorobek W, Kotowska M, Starace M, Piraccini B, Olszewska M et al. Cardiovascular risk in patients with alopecia areata: a cross-sectional study. Dermatology Review/Przegląd Dermatologiczny. 2023;110(2):133-141. doi:10.5114/dr.2023.128016.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| 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 | 0.001 |
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 it