Chest Pain in Children: Is It Another “Growing Pain”?
Bibliographic record
Abstract
Chest pain is a common complaint among children that has a non-cardiac origin in 99% of pediatric cases. We conducted a literature review of the different proposed etiologies of pediatric chest pain, as well as the evidence base supporting current approaches. Among the non-cardiac causes of chest pain in children, musculoskeletal causes are reported to be the most prevalent. This includes precordial catch syndrome, Tietze's syndrome, and costochondritis. However, these origins of musculoskeletal chest pain were described historically, and their labels are likely applied too broadly. It is important that providers be able to differentiate between benign chest pain that truly has a musculoskeletal origin and that which lacks an identifiable cause. To determine the cause of chest pain, providers should take a detailed history, physical examination, electrocardiogram, and any additional indicated laboratory tests. Musculoskeletal chest pain should only be diagnosed if there is an objective finding of reproducible tenderness during the physical examination or if there is a plausible history. If no cause can be identified, the chest pain may be linked to somatization. As a result, these patients may benefit from psychiatric evaluation and mindfulness-based interventions. To better inform clinical care, providers should be aware of these emerging management approaches.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".