CRT-D implantation: one procedure, two problems
Bibliographic record
Abstract
ENWEndNote BIBJabRef, Mendeley RISPapers, Reference Manager, RefWorks, Zotero AMA Engel A, Machejek J, Ząbek A, Kozanecki A, Lelakowski J, Małecka B. CRT-D implantation: one procedure, two problems. Advances in Interventional Cardiology/Postępy w Kardiologii Interwencyjnej. 2016;12(3):282-284. doi:10.5114/aic.2016.61657. APA Engel, A., Machejek, J., Ząbek, A., Kozanecki, A., Lelakowski, J., & Małecka, B. (2016). CRT-D implantation: one procedure, two problems. Advances in Interventional Cardiology/Postępy w Kardiologii Interwencyjnej, 12(3), 282-284. https://doi.org/10.5114/aic.2016.61657 Chicago Engel, Anna M., Jakub Machejek, Andrzej Ząbek, Artur Kozanecki, Jacek Lelakowski, and Barbara Małecka. 2016. "CRT-D implantation: one procedure, two problems". Advances in Interventional Cardiology/Postępy w Kardiologii Interwencyjnej 12 (3): 282-284. doi:10.5114/aic.2016.61657. Harvard Engel, A., Machejek, J., Ząbek, A., Kozanecki, A., Lelakowski, J., and Małecka, B. (2016). CRT-D implantation: one procedure, two problems. Advances in Interventional Cardiology/Postępy w Kardiologii Interwencyjnej, 12(3), pp.282-284. https://doi.org/10.5114/aic.2016.61657 MLA Engel, Anna M. et al. "CRT-D implantation: one procedure, two problems." Advances in Interventional Cardiology/Postępy w Kardiologii Interwencyjnej, vol. 12, no. 3, 2016, pp. 282-284. doi:10.5114/aic.2016.61657. Vancouver Engel A, Machejek J, Ząbek A, Kozanecki A, Lelakowski J, Małecka B. CRT-D implantation: one procedure, two problems. Advances in Interventional Cardiology/Postępy w Kardiologii Interwencyjnej. 2016;12(3):282-284. doi:10.5114/aic.2016.61657.
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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 | 0.017 | 0.041 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.009 | 0.011 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.010 | 0.018 |
| Insufficient payload (model declined to judge) | 0.018 | 0.015 |
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".