Hypotonic or isotonic maintenance fluids for paediatric patients: the never-ending story
Bibliographic record
Abstract
ENWEndNote BIBJabRef, Mendeley RISPapers, Reference Manager, RefWorks, Zotero AMA Langer T, L. Malbrain M, Van Regenmortel N. Hypotonic or isotonic maintenance fluids for paediatric patients: the never-ending story. Anaesthesiology Intensive Therapy. 2020;52(5):357-358. doi:10.5114/ait.2020.101362. APA Langer, T., L. Malbrain, M., & Van Regenmortel, N. (2020). Hypotonic or isotonic maintenance fluids for paediatric patients: the never-ending story. Anaesthesiology Intensive Therapy, 52(5), 357-358. https://doi.org/10.5114/ait.2020.101362 Chicago Langer, Thomas, Manu L. Malbrain, and Niels Van Regenmortel. 2020. "Hypotonic or isotonic maintenance fluids for paediatric patients: the never-ending story". Anaesthesiology Intensive Therapy 52 (5): 357-358. doi:10.5114/ait.2020.101362. Harvard Langer, T., L. Malbrain, M., and Van Regenmortel, N. (2020). Hypotonic or isotonic maintenance fluids for paediatric patients: the never-ending story. Anaesthesiology Intensive Therapy, 52(5), pp.357-358. https://doi.org/10.5114/ait.2020.101362 MLA Langer, Thomas et al. "Hypotonic or isotonic maintenance fluids for paediatric patients: the never-ending story." Anaesthesiology Intensive Therapy, vol. 52, no. 5, 2020, pp. 357-358. doi:10.5114/ait.2020.101362. Vancouver Langer T, L. Malbrain M, Van Regenmortel N. Hypotonic or isotonic maintenance fluids for paediatric patients: the never-ending story. Anaesthesiology Intensive Therapy. 2020;52(5):357-358. doi:10.5114/ait.2020.101362.
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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 | 0.011 | 0.069 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.006 | 0.014 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.011 | 0.028 |
| Insufficient payload (model declined to judge) | 0.017 | 0.011 |
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".