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Record W2207743554 · doi:10.1155/2009/915302

Improving the Assessment of Pediatric Chronic Pain: Harnessing the Potential of Electronic Diaries

2009· review· en· W2207743554 on OpenAlexafffund
Jennifer Stinson

Bibliographic record

VenuePain Research and Management · 2009
Typereview
Languageen
FieldMedicine
TopicPediatric Pain Management Techniques
Canadian institutionsSickKids FoundationHospital for Sick ChildrenUniversity of Toronto
FundersCanadian Institutes of Health ResearchCanadian Child Health Clinician Scientist Program
KeywordsUsabilityChronic painElectronic data capturePain assessmentCurrent (fluid)Computer scienceData scienceMedicinePhysical therapyPain managementAlternative medicineHuman–computer interactionEngineering

Abstract

fetched live from OpenAlex

Current methods for evaluating chronic pain in children suffer from methodological problems. Real-time data capture approaches using electronic diaries have been proposed as a new standard for pain measurement. However, there is limited information available regarding the development, feasibility and validity of these approaches in children. The present paper reviews problems with current measures; rationale for developing real-time data capture approaches using electronic diaries; mechanics of developing electronic pain diaries; current evidence regarding their usability, feasibility and validity; and discusses future directions for research in this area.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.977
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0310.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
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 head0.047
GPT teacher head0.394
Teacher spread0.347 · 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

Citations55
Published2009
Admission routes2
Has abstractyes

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