Additional file 1 of The interaction between arterial oxygenation and carbon dioxide and hospital mortality following out of hospital cardiac arrest: a cohort study
Bibliographic record
Abstract
Additional file 1: The interaction between arterial oxygenation and carbon dioxide and hospital mortality following out of hospital cardiac arrest; a cohort study. Provides details on variables used in the logistic regression analysis and of how PaCO2 modified the PaO2/FiO2 – mortality and PaO2 – mortality relationships. Sensitivity analyses are presented examining the effects of varying thresholds of hyperoxia on mortality and the effect of including those who died in the first 24 hours. Table 1 Variables used in logistic regression analysis Model-1 and Model-2. aAPACHE II severe co-morbidities include the following: 1. Liver: biopsy proven cirrhosis with portal hypertension, previous upper gastrointestinal bleeding secondary to portal hypertension, previous hepatic encephalopathy or failure. 2. Cardiovascular: New York Heart Association Classification IV. 3. Respiratory: pulmonary disease resulting in severe exercise limitation, chronic hypoxemia or hypercapnia, severe pulmonary hypertension or ventilator dependency. 4. Renal: Dialysis dependent renal failure. 5. Immunocompromised: either drug or disease induced. Table 2 Odds ratio for mortality for six pH categories used in logistic regression analysis Model-1. Table 3 Adjusted odds ratio for mortality PaO2/FiO2 versus PaCO2 derived using logistic regression Model-1. Table 4 Adjusted odds ratio for mortality PaO2 versus PaCO2 derived using logistic regression Model-2. Table 5 Sensitivity analysis; Impact of varying thresholds of hyperoxia on hospital mortality. Five PaO2 categories were chosen to examine the impact of alternative thresholds of hyperoxemia on mortality. Table 6 Sensitivity analysis; Adjusted odds ratio for mortality for varying thresholds of PaO2 versus PaCO2. Five PaO2 categories were chosen to examine the impact of alternative thresholds of hyperoxemia on mortality, PaCO2 categories were included to test for interaction. aDue to the small number of patients in this subcategory of the sensitivity analysis (all of whom died), the interaction term has been dropped from the model and the category therefore has the same OR for mortality as the reference category. Table 7 Sensitivity analysis; Impact of PaO2/FiO2, PaO2 and PaCO2 on hospital mortality including those who died within the first 24 hours. Table 8 Sensitivity analysis; Adjusted odds ratio for mortality PaO2/FiO2 versus PaCO2 including those who died within the first 24 hours. Table 9 Sensitivity analysis; Adjusted odds ratio for mortality PaO2 versus PaCO2 including those who died within the first 24 hours. Here, the ability of PaCO2 to modify mortality relationships is attenuated. Table 10 Sensitivity analysis; Impact of varying thresholds of hyperoxia on hospital mortality including those who died within the first 24 hours. Five PaO2 categories were chosen to examine the impact of alternative thresholds of hyperoxemia on mortality including those who died within the first 24 hours. Table 11 Sensitivity analysis; Adjusted odds ratio for mortality for varying thresholds of PaO2 versus PaCO2 including those who died within the first 24 hours. Here, the ability of PaCO2 to modify mortality relationships is attenuated. aDue to the small number of patients in this subcategory of the sensitivity analysis (all of whom died), the interaction term has been dropped from the model and the category therefore has the same OR for mortality as the reference category.
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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 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.718 | 0.037 |
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".