Commentary: The Importance of Sleep in Pediatric Chronic Pain--A Wake-up Call for Pediatric Psychologists
Bibliographic record
Abstract
Historically, sleep problems were not commonly considered as a core component of pediatric chronic pain, in contrast to other well-established correlates, such as physical disability, depression, anxiety, and family disruption. At best, when they were considered, sleep disturbances were viewed as a secondary problem with questionable impact on pediatric pain management. It has been only recently that reviews and empirical studies have drawn our attention to the importance of sleep as a variable that can have a significant influence on, and be influenced by, pediatric chronic pain (e.g., Gagliese & Chambers, 2007; Huntley, Campo, Dahl, & Lewin, 2007; Lewin & Dahl, 1999; Meltzer, Logan, & Mindell, 2005; Miller, Palermo, Powers, Scher, & Hershey, 2003; Palermo & Kiska, 2005). The four studies on sleep and pain published in this special issue (Long et al., this issue; Tsai et al., this issue; Valrie et al., this issue; Ward et al., this issue) together serve to reinforce and extend our current understanding of the importance of sleep in pediatric chronic pain. The studies apply diverse methodologies for assessing sleep (i.e., self-report questionnaires, daily diaries, actigraphy, and polysomnography) across several different chronic pain conditions (i.e., arthritis, musculoskeletal pain, sickle cell disease, and headache), yet the results send a consistent message. Sleep problems are common in children with chronic pain and are related to mood disturbances and difficulties with daily functioning. The application of advanced statistical techniques such as multilevel modeling (Valrie et al., this issue) further highlights the complex interrelationships among sleep, pain, and other variables such as mood.
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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 | 0.056 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.004 | 0.008 |
| Open science | 0.007 | 0.002 |
| Research integrity | 0.050 | 0.059 |
| Insufficient payload (model declined to judge) | 0.008 | 0.006 |
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".