Bibliographic record
Abstract
April 2021–another exciting and informative issue of Pediatric Critical Care Medicine (PCCM). There are three articles for this month’s Editor’s Choice. Please also look at the announcement about PCCM Narratives, Letters, and Correspondence in the PCCM Notes, Methods, and Statistics section (1). Each of my Editor’s Choice articles are focused on the theme of teamwork and practice improvement. In PCCM’s Online First section, over the last few months, these articles have already attracted significant attention. The first article is about real-time coaching and cardiopulmonary resuscitation (CPR) in the setting of simulated pediatric cardiac arrest (2). The second article reports on a before-versus-after study focused on standardizing work rounds in the PICU (3). My third choice describes sustainability and lessons learned 3 years after implementing an early mobilization program in the PICU (4). All three articles (2–4) and their related editorials (5–7) will be freely available for the next 2 months. DOES THE ADDITION OF A TRAINED CPR COACH TO PEDIATRIC RESUSCTIATION TEAMS HAVE THE POTENTIAL TO MAKE A DIFFERENCE? Kessler DO, Grabinski Z, Shephard LN, et al: Influence of Cardiopulmonary Resuscitation on Interruptions in Chest Compressions During Simulated Pediatric Cardiac Arrest (2). This article describes the consequence of randomization of CPR teams in a cardiac arrest simulation, to either include the presence of a trained CPR coach, or to have an additional member who does not work as a coach. The study involved four centers in Canada and the United States, with 200 health care professionals participating in a number of 5-person resuscitation teams. Our editorialists discuss the implications of the results but, more importantly, the need for a paradigm shift in how we think about team support and activity and the application of simulation research to real-life resuscitation events (5). ARE THERE BENEFITS TO STANDARDIZING PICU WORK ROUNDS? Lucrezia S, Noether J, Sochet AA: Standardized Work Rounds Enhance Teaming, Comprehensiveness, Shared Mental Model Development, and Achievement Rate of End-of-Shift Goals (3). This article describes two pre-to-post Plan-Do-Study-Act cycles involving 154 PICU work round encounters with a nurse-led, team-science approach (definitely look at Figure 1 in the article for a clearer understanding). Our editorialists help us to see the importance of the report and ask two questions related to everyday clinical care: ‘what could we do better?’ and ‘how does better teamwork lead to better patient care?’ (6). CAN WE INITIATE, INTEGRATE AND SUSTAIN THE PICU-CULTURE CHANGES NEEDED FOR PRACTICING EARLY PATIENT MOBILIZATION? Patel RV, Redivo J, Nelliot A, et al: Early Mobilization in a PICU: A Qualitative Sustainability Analysis of PICU Up! (4) The third article is a much-needed follow-up detailing interprofessional perspectives, engagement, and practice 3 years after starting a PICU mobilization program. (The figures are definitely worth reviewing; in color too). The accompanying editorial is written by Dr. Brenda Morrow, one of the new Senior Associate Editors at PCCM, who guides us through the challenges and necessity of qualitative research (70. This month, the three Editor’s Choice articles serve as important updates for general education in what’s new in research related to PICU teamwork in everyday practice, these are: the potential benefits of a coach for real-time CPR (2); the reframing of work rounds in the PICU (3); and, real-world sustainability of new practice initiatives such as early mobilization (4). All are good reading material as evidenced by their already impressive Altmetric scores.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 | 0.052 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.007 | 0.006 |
| Insufficient payload (model declined to judge) | 0.295 | 0.142 |
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".