Assessing a child or adolescent with low back pain is different to assessing an adult with low back pain
Bibliographic record
Abstract
In contrast to an assessment of an adult presenting with low back pain (LBP), clinicians should utilise different approaches when assessing children and adolescents presenting with LBP. Children are not 'little adults'. There are some unique pathologies that only occur in this age group: (i) serious pathologies include infection, fracture, child abuse and malignancy; (ii) growth-related pathologies include scoliosis, Scheuermann's disease, pars fracture and spondylolysis; and (iii) rheumatological conditions include juvenile idiopathic arthritis and ankylosing spondylitis. With changes in each child occurring physically, emotionally and socially, a clinician's knowledge of typical developmental milestones is essential to identify regression or delayed development. When listening to a child discuss their pain experience, a flexible structure should be implemented that gives the capacity to actively listen to a child's narrative (and that of their guardian) and to conduct an effective physical examination. This viewpoint also summarises the relationship between potential clinical diagnoses and key elements of a physical examination. Deciding on the type and timing of paediatric-specific physical examination tests requires unique child-centred considerations. Paediatric-specific outcome measures should be used but implemented pragmatically, with consideration regarding the time, complexity and pathology suspected. Systematic and rigorous approaches to both treatment planning and re-assessment are then proposed for the assessment of children and adolescents presenting with LBP.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".