Defining Growing Pains: A Scoping Review
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Up to one third of children may be diagnosed with growing pains, but considerable uncertainty surrounds how to make this diagnosis. The objective of this study was to detail the definitions of growing pains in the medical literature. METHODS: Scoping review with 8 electronic databases and 6 diagnostic classification systems searched from their inception to January 2021. The study selection included peer-reviewed articles or theses referring to "growing pain(s)" or "growth pain(s)" in relation to children or adolescents. Data extraction was performed independently by 2 reviewers. RESULTS: We included 145 studies and 2 diagnostic systems (ICD-10 and SNOMED). Definition characteristics were grouped into 8 categories: pain location, age of onset, pain pattern, pain trajectory, pain types and risk factors, relationship to activity, severity and functional impact, and physical examination and investigations. There was extremely poor consensus between studies as to the basis for a diagnosis of growing pains. The most consistent component was lower limb pain, which was mentioned in 50% of sources. Pain in the evening or night (48%), episodic or recurrent course (42%), normal physical assessment (35%), and bilateral pain (31%) were the only other components to be mentioned in more than 30% of articles. Notably, more than 80% of studies made no reference to age of onset in their definition, and 93% did not refer to growth. Limitations of this study are that the included studies were not specifically designed to define growing pains. CONCLUSIONS: There is no clarity in the medical research literature regarding what defines growing pain. Clinicians should be wary of relying on the diagnosis to direct treatment decisions.
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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 | 0.021 | 0.087 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.031 | 0.026 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".