Implementation of veno-venous extracorporeal membrane oxygenation in a COVID-19 convalescent
Bibliographic record
Abstract
ENWEndNote BIBJabRef, Mendeley RISPapers, Reference Manager, RefWorks, Zotero AMA Gawda R, Marszalski M, Molsa M, et al. Implementation of veno-venous extracorporeal membrane oxygenation in a COVID-19 convalescent. Anaesthesiology Intensive Therapy. 2020;52(3):253-255. doi:10.5114/ait.2020.97956. APA Gawda, R., Marszalski, M., Molsa, M., Piwoda, M., Pietka, M., & Gawor, M. et al. (2020). Implementation of veno-venous extracorporeal membrane oxygenation in a COVID-19 convalescent. Anaesthesiology Intensive Therapy, 52(3), 253-255. https://doi.org/10.5114/ait.2020.97956 Chicago Gawda, Ryszard, Maciej Marszalski, Maciej Molsa, Maciej Piwoda, Marek Pietka, Maciej Gawor, and Wojciech Mielnicki et al. 2020. "Implementation of veno-venous extracorporeal membrane oxygenation in a COVID-19 convalescent". Anaesthesiology Intensive Therapy 52 (3): 253-255. doi:10.5114/ait.2020.97956. Harvard Gawda, R., Marszalski, M., Molsa, M., Piwoda, M., Pietka, M., Gawor, M., Mielnicki, W., Dyla, A., and Czarnik, T. (2020). Implementation of veno-venous extracorporeal membrane oxygenation in a COVID-19 convalescent. Anaesthesiology Intensive Therapy, 52(3), pp.253-255. https://doi.org/10.5114/ait.2020.97956 MLA Gawda, Ryszard et al. "Implementation of veno-venous extracorporeal membrane oxygenation in a COVID-19 convalescent." Anaesthesiology Intensive Therapy, vol. 52, no. 3, 2020, pp. 253-255. doi:10.5114/ait.2020.97956. Vancouver Gawda R, Marszalski M, Molsa M, Piwoda M, Pietka M, Gawor M et al. Implementation of veno-venous extracorporeal membrane oxygenation in a COVID-19 convalescent. Anaesthesiology Intensive Therapy. 2020;52(3):253-255. doi:10.5114/ait.2020.97956.
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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 | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 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".