Additional file 1 of The interaction between arterial oxygenation and carbon dioxide and hospital mortality following out of hospital cardiac arrest: a cohort study
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,035 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,003 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,718 | 0,037 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».