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Record W2078296345 · doi:10.1586/14737167.3.3.327

Non-communicating children’s pain checklist: better pain assessment for severely disabled children

2003· article· en· W2078296345 on OpenAlexaff
Lynn M. Breau

Bibliographic record

VenueExpert Review of Pharmacoeconomics & Outcomes Research · 2003
Typearticle
Languageen
FieldMedicine
TopicPediatric Pain Management Techniques
Canadian institutionsIzaak Walton Killam Health Centre
Fundersnot available
KeywordsChecklistObservational studyPhysical therapyPain assessmentMedicineCognitionPain catastrophizingPsychologyPain managementPhysical medicine and rehabilitationChronic painPsychiatry

Abstract

fetched live from OpenAlex

Advances in the treatment of pain for children with severe disabilities have lagged behind that for other children. This is due, in part, to a lack of valid assessment tools for their pain. The non-communicating children's pain checklists are observational pain tools that were developed specifically for children with severe disabilities who are unable to communicate verbally. The non-communicating children's pain checklist-revised has been validated for use with a wide range of pain types in the home. The non-communicating children's pain checklist-postoperative version is used for pain following surgery in the hospital setting. Scores for determining the presence of pain have also been developed. They are appropriate for children with varying degrees of physical, cognitive and communicative impairments.

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)0.031
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: none
Teacher disagreement score0.206
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0310.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.032
GPT teacher head0.492
Teacher spread0.459 · 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 designNot applicable
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

Citations25
Published2003
Admission routes1
Has abstractyes

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