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Record W4214768352 · doi:10.1111/jpc.15933

Assessing a child or adolescent with low back pain is different to assessing an adult with low back pain

2022· article· en· W4214768352 on OpenAlexaff
Joshua W. Pate, Rhiannon Joslin, David Anderson

Bibliographic record

VenueJournal of Paediatrics and Child Health · 2022
Typearticle
Languageen
FieldMedicine
TopicPediatric Pain Management Techniques
Canadian institutionsMcMaster UniversityMcMaster University Medical Centre
Fundersnot available
KeywordsMedicineSpondylolysisPhysical examinationLow back painPhysical therapyScoliosisPediatricsSpondylolisthesisAlternative medicinePsychiatrySurgeryPathology

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.138
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.017
GPT teacher head0.301
Teacher spread0.284 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations10
Published2022
Admission routes1
Has abstractyes

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