Management of tracheo-innominate artery fistula in the challenging environment of an improvised COVID-19 intensive care unit
Bibliographic record
Abstract
AMA Krzelj K, Duric Z, Situm I, et al. Management of tracheo-innominate artery fistula in the challenging environment of an improvised COVID-19 intensive care unit. Anaesthesiology Intensive Therapy. 2023;55(4):310-312. doi:10.5114/ait.2023.132916. APA Krzelj, K., Duric, Z., Situm, I., Karmelic, D., Erceg, A., & Kljajic, K. et al. (2023). Management of tracheo-innominate artery fistula in the challenging environment of an improvised COVID-19 intensive care unit. Anaesthesiology Intensive Therapy, 55(4), 310-312. https://doi.org/10.5114/ait.2023.132916 Chicago Krzelj, Kristina, Zeljko Duric, Ivan Situm, Dora Karmelic, Ante Erceg, Kristina Kljajic, and Mirabel Mazar et al. 2023. "Management of tracheo-innominate artery fistula in the challenging environment of an improvised COVID-19 intensive care unit". Anaesthesiology Intensive Therapy 55 (4): 310-312. doi:10.5114/ait.2023.132916. Harvard Krzelj, K., Duric, Z., Situm, I., Karmelic, D., Erceg, A., Kljajic, K., Mazar, M., Gasparovic, H., and Lovric, D. (2023). Management of tracheo-innominate artery fistula in the challenging environment of an improvised COVID-19 intensive care unit. Anaesthesiology Intensive Therapy, 55(4), pp.310-312. https://doi.org/10.5114/ait.2023.132916 MLA Krzelj, Kristina et al. "Management of tracheo-innominate artery fistula in the challenging environment of an improvised COVID-19 intensive care unit." Anaesthesiology Intensive Therapy, vol. 55, no. 4, 2023, pp. 310-312. doi:10.5114/ait.2023.132916. Vancouver Krzelj K, Duric Z, Situm I, Karmelic D, Erceg A, Kljajic K et al. Management of tracheo-innominate artery fistula in the challenging environment of an improvised COVID-19 intensive care unit. Anaesthesiology Intensive Therapy. 2023;55(4):310-312. doi:10.5114/ait.2023.132916.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".