Anaemia due to gastric haemorrhage by a metastatic Merkel cell carcinoma
Bibliographic record
Abstract
AMA Santos-Juanes J, Palomo C, Rodrigo L, Fernández-Vega I. Anaemia due to gastric haemorrhage by a metastatic Merkel cell carcinoma. Gastroenterology Review/Przegląd Gastroenterologiczny. 2015;10(4):244-246. doi:10.5114/pg.2015.51183. APA Santos-Juanes, J., Palomo, C., Rodrigo, L., & Fernández-Vega, I. (2015). Anaemia due to gastric haemorrhage by a metastatic Merkel cell carcinoma. Gastroenterology Review/Przegląd Gastroenterologiczny, 10(4), 244-246. https://doi.org/10.5114/pg.2015.51183 Chicago Santos-Juanes, Jorge, Carmen Palomo, Luis Rodrigo, and Iván Fernández-Vega. 2015. "Anaemia due to gastric haemorrhage by a metastatic Merkel cell carcinoma". Gastroenterology Review/Przegląd Gastroenterologiczny 10 (4): 244-246. doi:10.5114/pg.2015.51183. Harvard Santos-Juanes, J., Palomo, C., Rodrigo, L., and Fernández-Vega, I. (2015). Anaemia due to gastric haemorrhage by a metastatic Merkel cell carcinoma. Gastroenterology Review/Przegląd Gastroenterologiczny, 10(4), pp.244-246. https://doi.org/10.5114/pg.2015.51183 MLA Santos-Juanes, Jorge et al. "Anaemia due to gastric haemorrhage by a metastatic Merkel cell carcinoma." Gastroenterology Review/Przegląd Gastroenterologiczny, vol. 10, no. 4, 2015, pp. 244-246. doi:10.5114/pg.2015.51183. Vancouver Santos-Juanes J, Palomo C, Rodrigo L, Fernández-Vega I. Anaemia due to gastric haemorrhage by a metastatic Merkel cell carcinoma. Gastroenterology Review/Przegląd Gastroenterologiczny. 2015;10(4):244-246. doi:10.5114/pg.2015.51183.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".