Notice bibliographique
Résumé
What an amazing issue of Pediatric Critical Care Medicine (PCCM) to finish volume 23, 2022. Your December reading could cover three key themes: sepsis and shock, respiratory care and supportive ventilation, and multicenter clinical research groups. There are six articles about sepsis and shock (1−6). I’m using only four of the sepsis/shock themed articles along with one accompanying editorial (7) as my Editor’s Choice this month, but I encourage you to read all the articles in this theme. The other two themes are featured in the section below titled PCCM Connections for Readers. DO WE HAVE AN ASSESSMENT OF TIMECOURSE IN SEPSIS-BIOMARKER-PHENOTYPE AND RISK-CATEGORY USING READILY AVAILABLE DIAGNOSTICS? Horvat CM, Fabio A, Nagin DS, et al; Eunice Kennedy Shriver National Institute of Child Health and Human Development Collaborative Pediatric Critical Care Research Network: Mortality Risk in Pediatric Sepsis Based on C-Reactive Protein and Ferritin Levels (1). My first Editor’s Choice article comes from the nine-center Eunice Kennedy Shriver National Institutes of Child Health and Development Collaborative Pediatric Critical Care Research Network (CPCCRN) and their study of phenotyping pediatric sepsis-induced multiple organ failure study (PHENOMS). Two-hundred fifty-three children with two measurements of C-reactive protein and ferritin level, within one week, were categorized into five distinct trajectories, and associated outcomes were related to each of these groupings. ARE WE ABLE TO DIFFERENTIATE TIMECOURSE AND TRAJECTORY IN VASOACTIVE-DEPENDENT-SHOCK? Perizes EN, Chong G, Sanchez-Pinto LN: Derivation and Validation of Vasoactive Inotrope Score Trajectory Groups In Critically Ill Children With Shock (2). My next Editor’s Choice article is from two centers with a dataset of over 1,800 non-cardiac patients who were treated with vasoactive infusions within 24 hours of PICU admission. The authors have looked at hourly vasoactive inotropic scores (8) over the first 72 hours of treatment with vasopressors. And, rather like the biomarker article this month (1), distinct trajectories were identified and associated with risk factors, response to therapy, and outcomes. CAN WE IDENTIFY AND RISK-STRATIFY PATIENTS WITH SEPSIS AND ACCOMPANYING ACUTE BRAIN DYSFUNCTION? Alcamo AA, Barren GJ, Becker AE, et al: Validation of a Computational Phenotype to Identify Acute Brain Dysfunction in Pediatric Sepsis (3). My next Editor’s Choice article used a single-center dataset of over 4,200 index sepsis episodes to validate a computational phenotype that identifies and reflects the full spectrum of acute brain dysfunction during sepsis. The work builds on previous research reported in PCCM (9,10) and helps our field to make progress in studying the problem of neurologic or behavioral changes in sepsis. There is no related editorial but do re-read the two previous commentaries on “sepsis encephalopathy,” which remain relevant today (11,12). CAN WE RISK-STRATIFY FEBRILE CHILDREN IN THE EMERGENCY DEPARTMENT WHO GO ON TO REQUIRE RESUSCITATION AND PICU ADMISSION USING MEASURABLE BIOMARKERS? Lenihan RAF, Ang J, Pallmann P, et al: Mid-Regional Pro-Adrenomedullin in Combination With Pediatric Early Warning Scores for Risk Stratification of Febrile Children Presenting to the Emergency Department: Secondary Analysis of a Non-Prespecified United Kingdom Cohort Study (4). My final Editor’s Choice article is a non-prespecified secondary analysis of a dataset from a UK emergency department (ED) cohort study. The authors looked at over 1,100 cases presenting with fever in which 21% had definite or probable bacterial infection, 12% had fluid resuscitation, and 4% were admitted to the PICU or high-dependency care area. The exploratory analyses have examined these three outcomes and determined whether presenting levels of procalcitonin and mid-regional pro-adrenomedullin added to the risk stratification using a clinical assessment with pediatric early warning scoring. The accompanying editorial (7) not only provides a detailed discussion about bringing clinical risk stratification tools to the bedside, but also highlights the need for more collaborative research covering the continuum of care from ED to PICU admission. PCCM CONNECTIONS FOR READERS The connections material this month focuses on two themes in PCCM research–respiratory care and supportive ventilation, and the work of multicenter clinical research groups. First, there are five articles to use as updates in respiratory and ventilatory practice. We have an article about fluid accumulation during mechanical ventilation (13), an article about prolonged mechanical ventilation in pediatric trauma (14), and a feature meta-narrative review about the clinical challenges in “liberation” from pediatric ventilation (15). The other two articles that I’m really excited about–not that all articles aren’t exciting–because they add worthwhile reading in the PCCM Trials section. In 2021, Rotta and Shein (16) and Kneyber (17) challenged our clinical community about the pervasive use of high-flow nasal cannula (HFNC) oxygen therapy as post-extubation support for infants with bronchiolitis, given the absence of a randomized controlled trial (RCT) of this intervention. Then, earlier this year Ramnarayan and associates from the UK Paediatric Critical Care Society Study Group (PCCS-SG) reported the results of two RCTs: 1) HFNC therapy versus continuous positive airway pressure (CPAP) therapy on liberation from respiratory support in acutely ill children admitted to the PICU (i.e., the “step-up” FIRST-ABC trial) (18); and 2) HFNC therapy versus CPAP following extubation on liberation from respiratory support in critically ill children (i.e., the “step-down” FIRST-ABC trial) (19). I asked our 2021 PCCM authors to provide a Commentary in the PCCM Trials section on their current perspective of the new RCTs in the context of their previous writing, as well as to tell us how they plan to use the new information from the FIRST-ABC trials in their practice (20). Alongside that Commentary, Ramnarayan and Peters–FIRST-ABC investigators–answer the same questions in their own Commentary (21). Everyone will have a view. As Franklin and Schibler stated in their March 2022 PCCM editorial about HFNC therapy, “Where and by whom supportive respiratory care is provided depends on hospital internal guidelines and local practice” (22). At PCCM we are therefore interested in seeing large scale observational research on whether the FIRST-ABC trial findings are being implemented (or not). Second, there are three Special Articles grouped together in a PCCM Mini Symposium about clinical research groups in pediatric critical care (23−25). The purpose is to provide insights into how these groups function and work, describe their focus, and talk about lessons learned that other clinical research groups across the PICU world can consider as they develop their own consortia. The first article comes from the US Eunice Kennedy Shriver National Institutes of Child Health and Development funded CPCCRN, which already features as one of my Editor’s Choice articles in this issue (see PHENOMS [1]). PCCM published an inaugural article about CPCCRN in 2006 (26), and it is well worth reading that Special Article before moving on to the evolution and major developments described in the contemporary piece (23). The second article comes from the Pediatric Acute Lung Injury and Sepsis Investigators’ (PALISI) network, which celebrated its 20th Anniversary in 2022. The PALISI network is a US and Canada investigator-led organization, with international collaborations. It has undergone significant changes in organization over the past 20 years, and its research interests go far beyond its naming. A current emphasis is also a commitment to education and training the next generation of PICU research investigators (24). The third article comes from the UK PCCS-SG, which is featured in this month’s PCCM Trials section (20,21). Here the scale of PICU practice differs from the US and North America setting, and readers may be interested to read about a different approach to collaborative research. This contribution focuses on the unique environment and system of healthcare that engendered a 20-year journey toward delivering 13 multicenter RCTs covering a spectrum of study designs, methodologies, and scale (25). The emphasis now is so-called “pragmatic clinical trials.” Having read the above highlighted content, and before you move through yet another great issue of PCCM from our authors, reviewers, and editors, please read the Narrative Essay called “Can You Hold Him?” (27). And finally, I have written a foreword to the December 2022 issue that will give some insight into the processes and metrics of PCCM (28).
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 | 0,004 | 0,046 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,001 |
| Méta-épidémiologie (sens large) | 0,002 | 0,002 |
| Bibliométrie | 0,003 | 0,001 |
| Études des sciences et des technologies | 0,002 | 0,001 |
| Communication savante | 0,008 | 0,004 |
| Science ouverte | 0,002 | 0,002 |
| Intégrité de la recherche | 0,006 | 0,007 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,197 | 0,110 |
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 ».