Notice bibliographique
Résumé
Sir, In reply to the comments by Morgan, we generally concur and offer the following additional commentary. We agree that the addition of patient weight, hourly urine output and baseline serum creatinine as core variables to the Australia New Zealand Intensive Care Society (ANZICS) Adult Patient Database (APD) would have tremendous value and certainly advance its capability for additional evaluation of acute kidney injury (AKI) and other kidney-related issues. At the time of analysis, however, these variables were not available [1]. Accordingly, assumptions about the data and their application to calculate the RIFLE categories were necessary. We recognize these assumptions potentially introduce some misclassification of the cohort and, as expected, influence incidence and outcome estimates. We, however, contend that any bias introduced due to misclassification resulting from these assumptions was likely to be balanced given they were applied systematically across the entire cohort. Moreover, the validated collection of these variables (i.e. patient weight, urine output, baseline serum creatinine) can be problematic. For example, the measurement of weight in critically ill patients is highly variable and context specific (i.e. ideal versus actual). Accurate estimates of pre-hospitalization baseline creatinine (or estimated glomerular filtration rate), in particular for those with chronic kidney disease, in critically ill patients are often impossible. Moreover, values at the time of ICU admission may be grossly modified by factors such as acute resuscitation. Likewise, the urine output can be modified by factors independent of kidney injury or function (i.e. fluid therapy, diuretic therapy). However, we also recognize that while the urine output criteria proposed for the RIFLE classification likely have significance, they have yet to be prospectively evaluated and validated. We appropriately acknowledge and discuss these limitations in our manuscript [2,3]. We are further reassured, however, by additional epidemiologic investigations that have found relative consistency in incidence rates and effect estimates for AKI and associated clinical outcomes with the RIFLE criteria (many having modified the original RIFLE criteria or omitting the urine output criteria altogether) [4,5]. We contend that our study is strengthened by inclusion of a very large heterogeneous cohort (over 120 000 critically ill patients) from multiple centres across Australia. As such, in the very least, it provides a broad estimate of the burden of early AKI (within 24 h of ICU admission) in critically ill patients. Finally, we certainly agree and would welcome additional prospective evaluation of the performance of the RIFLE criteria in similar cohorts of critically ill patients. Conflict of interest: None declared.
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 | 0,005 | 0,046 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,002 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,004 | 0,003 |
| Communication savante | 0,007 | 0,005 |
| Science ouverte | 0,002 | 0,004 |
| Intégrité de la recherche | 0,044 | 0,044 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,022 | 0,016 |
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 ».