Additional file 1 of The impact of sedative and vasopressor agents on cerebrovascular reactivity in severe traumatic brain injury
Bibliographic record
Abstract
Additional file 1: Appendix A. LOESS Curves – CPP/ICP/LPRx_10/LPRx_60. Appendix B. LOESS Curves – MAP/LPRx_15/LPRx_20/LPRx_30. Appendix C1. Multiple lnear model for all data. Appendix C2. Multiple linear model for sedatives data. Appendix C3. Multiple linear model for vasopressor data. Appendix D. Multiple linear model for segment Marshall CT score data. Appendix D1. Multiple linear model for Marshall CT data = 1. Appendix D2. Multiple linear model for Marshall CT data = 2. Appendix D3. Multiple linear model for Marshall CT data = 3. Appendix D5. Linear model for Marshall CT data = 5. Appendix E. One-Way ANOVA of physiology and Marshall CT score. Appendix F. Infusions of all data. Appendix G. Pre-time window over 50% time ICP > 20 mmHg. Appendix H. Pre-time window over 50% time ICP < 20 mmHg. Appendix I. Pre-time window over 50% time L-PRx_10 > 0. Appendix J. Pre-time window over 50% time L-PRx_10 < 0. Appendix K. Pre-time window over 50% time L-PRx_10 > 0.35. Appendix L. Pre-time window over 50% time L-PRx_10 < 0.35. Appendix M. Continuous infusion going from nothing to agent (and vice versa ie, On to Off). Appendix N. Assessing the High/Medium/Low of different infusion agent. Appendix O. Histogram distributions of continuous infusion agents.
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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 | 0.002 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.885 | 0.150 |
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".