Comprehensive Care in Pediatric Rheumatic Diseases: A Multifaceted Challenge
Bibliographic record
Abstract
Behavior is the most influential predictor for developing a medical condition and subsequently accounts for 40% of premature deaths1. Thus, simplistic approaches to complex health conditions are no longer viable or effective in producing optimal treatment outcomes. Successful psychological interventions have repeatedly demonstrated their profound effect on illness prevention, reduction of disease severity, and improved health-related quality of life2. Examples include cognitive behavioral therapy and acceptance and commitment therapy for pain, behavioral interventions to increase healthy lifestyle habits to treat obesity, and self-management support to increase treatment adherence. Despite decades of research, clinical anecdotes, and the World Health Organization’s biopsychosocial definition of health as “a state of complete physical, mental and social well-being and not merely the absence of disease or infirmity,”3 the lack of integrated care models across institutions perpetuates the dearth of behavioral healthcare, contributing to disease morbidity and mortality. Although society is shifting toward biopsychosocial healthcare approaches, the dualistic mind-body doctrine underlying the biomedical model remains ever-present4. The number of epidemics (e.g., obesity, diabetes, addiction) that continue to plague the United States, coupled with the generally lower level of US National Institutes of Health funding appropriated to mental health and pediatric institutes (e.g., National Institute of Mental Health, National Institute of Child Health and Human Development), are testaments to these systemic issues. It is of the utmost importance that comprehensive care is provided to promote quality of life for patients and the individuals affected and/or involved in care, particularly in pediatric health conditions5,6,7. However, despite dissemination of research, the translation and application into clinical care often lags behind, leaving many families with unmet needs. Providing well-rounded treatment for health conditions must be multifaceted8, hence the recent trend for multidisciplinary clinics. Yet, what … Address correspondence to K.L. Gamwell, Cincinnati Children’s Hospital Medical Center, Division of Behavioral Medicine & Clinical Psychology, Center for Adherence and Self-Management, 3333 Burnet Ave., MLC 7039, Cincinnati, OH 45229, USA. Email: kaitlyn.gamwell{at}cchmc.org.
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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 | 0.007 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.004 | 0.011 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".