Commentary on “An appraisal of neostigmine versus sugammadex for neuromuscular blockade reversal in patients with a prior heart transplant”
Bibliographic record
Abstract
AMA Chowdhury SR, Baidya DK. Commentary on “An appraisal of neostigmine versus sugammadex for neuromuscular blockade reversal in patients with a prior heart transplant”. Anaesthesiology Intensive Therapy. 2023. doi:10.5114/ait.2023.130638. APA Chowdhury, S. R., & Baidya, D. K. (2023). Commentary on “An appraisal of neostigmine versus sugammadex for neuromuscular blockade reversal in patients with a prior heart transplant”. Anaesthesiology Intensive Therapy. https://doi.org/10.5114/ait.2023.130638 Chicago Chowdhury, Sumit R, and Dalim K Baidya. 2023. "Commentary on “An appraisal of neostigmine versus sugammadex for neuromuscular blockade reversal in patients with a prior heart transplant”". Anaesthesiology Intensive Therapy. doi:10.5114/ait.2023.130638. Harvard Chowdhury, S., and Baidya, D. (2023). Commentary on “An appraisal of neostigmine versus sugammadex for neuromuscular blockade reversal in patients with a prior heart transplant”. Anaesthesiology Intensive Therapy. https://doi.org/10.5114/ait.2023.130638 MLA Chowdhury, Sumit et al. "Commentary on “An appraisal of neostigmine versus sugammadex for neuromuscular blockade reversal in patients with a prior heart transplant”." Anaesthesiology Intensive Therapy, 2023. doi:10.5114/ait.2023.130638. Vancouver Chowdhury S, Baidya D. Commentary on “An appraisal of neostigmine versus sugammadex for neuromuscular blockade reversal in patients with a prior heart transplant”. Anaesthesiology Intensive Therapy. 2023. doi:10.5114/ait.2023.130638.
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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 | 0.007 | 0.071 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.006 | 0.002 |
| Research integrity | 0.033 | 0.033 |
| Insufficient payload (model declined to judge) | 0.022 | 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".