Diabetic ketoacidosis after self-poisoning with bromadiolone
Bibliographic record
Abstract
ENWEndNote BIBJabRef, Mendeley RISPapers, Reference Manager, RefWorks, Zotero AMA Patoulias DI, Papchianou E, Michailidis T, Keryttopoulos P. Diabetic ketoacidosis after self-poisoning with bromadiolone. Archives of Medical Science - Civilization Diseases. 2018;3(1):1-2. doi:10.5114/amscd.2018.73210. APA Patoulias, D. I., Papchianou, E., Michailidis, T., & Keryttopoulos, P. (2018). Diabetic ketoacidosis after self-poisoning with bromadiolone. Archives of Medical Science - Civilization Diseases, 3(1), 1-2. https://doi.org/10.5114/amscd.2018.73210 Chicago Patoulias, Dimitrios I, Eleni Papchianou, Theodoros Michailidis, and Petros Keryttopoulos. 2018. "Diabetic ketoacidosis after self-poisoning with bromadiolone". Archives of Medical Science - Civilization Diseases 3 (1): 1-2. doi:10.5114/amscd.2018.73210. Harvard Patoulias, D., Papchianou, E., Michailidis, T., and Keryttopoulos, P. (2018). Diabetic ketoacidosis after self-poisoning with bromadiolone. Archives of Medical Science - Civilization Diseases, 3(1), pp.1-2. https://doi.org/10.5114/amscd.2018.73210 MLA Patoulias, Dimitrios et al. "Diabetic ketoacidosis after self-poisoning with bromadiolone." Archives of Medical Science - Civilization Diseases, vol. 3, no. 1, 2018, pp. 1-2. doi:10.5114/amscd.2018.73210. Vancouver Patoulias D, Papchianou E, Michailidis T, Keryttopoulos P. Diabetic ketoacidosis after self-poisoning with bromadiolone. Archives of Medical Science - Civilization Diseases. 2018;3(1):1-2. doi:10.5114/amscd.2018.73210.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".