Additional file 8 of An international RAND/UCLA expert panel to determine the optimal diagnosis and management of burn inhalation injury
Bibliographic record
Abstract
Additional file 8: Fig. S5. Appropriateness of diagnostic and management strategies for burn inhalation injury associated systemic toxicity. A Management strategies for burn inhalation injury associated carbon monoxide toxicity. B Indicators of burn inhalation injury associated hydrogen cyanide toxicity. C Management strategies for burn inhalation injury associated hydrogen cyanide toxicity. D Timing of hydroxocobalamin administration for varying severities of burn inhalation injury. Median ratings are presented (bold black line in each box) with the interquartile range (edges of box) and maximum and minimum ratings (extending vertical lines). Statements with median ratings of 1–3 were classed as inappropriate, 4–6 as uncertain and 7–9 as appropriate. Disagreement (disagreement index ≥ 1) was not present for any statements. Clinical suspicion of hydrogen cyanide toxicity was defined as low (normal blood lactate and the absence of potentially suspicious features), moderate (moderate lactatemia below 8 mmol/L and few potentially suspicious features) and high (hyperlactataemia ≥ 8 mmol/L and potentially suspicious features including anion gap lactic metabolic acidosis, altered consciousness, unexplained cardiac dysfunction). Panellists n = 15. DI, disagreement index; HCN, hydrogen cyanide.
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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 | 0.008 | 0.087 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.788 | 0.155 |
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".