Association of serum calcium level and serum uric acid level in psoriasis and its correlation with severity of psoriasis
Bibliographic record
Abstract
AMA Ambike J, Gosavi A, Pradhan S, Belgaumkar V. Association of serum calcium level and serum uric acid level in psoriasis and its correlation with severity of psoriasis. Dermatology Review/Przegląd Dermatologiczny. 2024;111(1):20-25. doi:10.5114/dr.2024.140793. APA Ambike, J., Gosavi, A., Pradhan, S., & Belgaumkar, V. (2024). Association of serum calcium level and serum uric acid level in psoriasis and its correlation with severity of psoriasis. Dermatology Review/Przegląd Dermatologiczny, 111(1), 20-25. https://doi.org/10.5114/dr.2024.140793 Chicago Ambike, Jheel, Anil Gosavi, Shekar Pradhan, and Vasudha Belgaumkar. 2024. "Association of serum calcium level and serum uric acid level in psoriasis and its correlation with severity of psoriasis". Dermatology Review/Przegląd Dermatologiczny 111 (1): 20-25. doi:10.5114/dr.2024.140793. Harvard Ambike, J., Gosavi, A., Pradhan, S., and Belgaumkar, V. (2024). Association of serum calcium level and serum uric acid level in psoriasis and its correlation with severity of psoriasis. Dermatology Review/Przegląd Dermatologiczny, 111(1), pp.20-25. https://doi.org/10.5114/dr.2024.140793 MLA Ambike, Jheel et al. "Association of serum calcium level and serum uric acid level in psoriasis and its correlation with severity of psoriasis." Dermatology Review/Przegląd Dermatologiczny, vol. 111, no. 1, 2024, pp. 20-25. doi:10.5114/dr.2024.140793. Vancouver Ambike J, Gosavi A, Pradhan S, Belgaumkar V. Association of serum calcium level and serum uric acid level in psoriasis and its correlation with severity of psoriasis. Dermatology Review/Przegląd Dermatologiczny. 2024;111(1):20-25. doi:10.5114/dr.2024.140793.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".