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Chronic non-cancer pain in children: we have a problem, but also solutions

2018· review· en· W2891052287 on OpenAlexaff
Eduardo A. Vega, Yves Beaulieu, Rachel Gauvin, Catherine Ferland, Stephanie Stabile, Rebecca Pitt, Víctor Hugo González Cárdenas, Pablo Ingelmo

Bibliographic record

VenueMinerva Anestesiologica · 2018
Typereview
Languageen
FieldMedicine
TopicPediatric Pain Management Techniques
Canadian institutionsMcGill University Health CentreMcGill UniversityMontreal Children's Hospital
Fundersnot available
KeywordsMedicineChronic painMultidisciplinary approachChronic diseaseDiseasePublic healthPhysical therapyCancerCancer painQuality of life (healthcare)Family medicineNursing

Abstract

fetched live from OpenAlex

Chronic non-cancer pain in children and adolescents has been described as "a modern public health disaster" that has generated significant medical and economic burdens within society. Seen as a disease in its own right, chronic pain has short and long-term consequences that impact not only the patient's health but also that of friends and families, due to significant parenting stress and disruptions in family life and structure. The evidence supporting pharmacological treatments and interventional procedures is limited, and no single strategy has been shown to be completely effective in children with chronic non-cancer pain. Therefore, considering the multifactorial nature of chronic pain, these patients should be treated with a multidisciplinary, balanced approach that seeks a primary outcome of improved functioning rather than of pain reduction. Using a bio-psycho-social approach, a multidisciplinary team, including a physiotherapist, nurse, social worker, psychologist, and physician, has been effective in achieving this outcome of improved functioning in children and adolescents with chronic pain. In this review, we discuss the impact, associated conditions, and evolution of chronic pain, along with the crucial role of every member of a multidisciplinary chronic pain clinic involved in the care of the children and adolescents with chronic non-cancer pain.

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

Direct model labels (unvalidated)

Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.

Model armCategoriesStudy designConfidence
gemmano category
Domain: not available · Genre: Review
About the Canadian research system: no · About a Canadian topic: no
Not applicablelow
gptno category
Domain: not available · Genre: Review
About the Canadian research system: no · About a Canadian topic: no
Other designlow
models splitAgreement compares identical category sets and study designs across arms.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.908
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.001
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 head0.058
GPT teacher head0.334
Teacher spread0.275 · 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

Labeled directly by 2 models reading the full record.

The models applied no category: nothing in the taxonomy fit this work.

The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.

Study designNot applicable · Other design
Domainnot available
GenreReview

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

Citations28
Published2018
Admission routes1
Has abstractyes

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