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Record W1550531151 · doi:10.1155/2006/197616

Children’s Self‐Reports of Pain Intensity: Scale Selection, Limitations and Interpretation

2006· review· en· W1550531151 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenuePain Research and Management · 2006
Typereview
Languageen
FieldMedicine
TopicPediatric Pain Management Techniques
Canadian institutionsUniversity of Saskatchewan
FundersInternational Association for the Study of Pain
KeywordsInterpretation (philosophy)Intensity (physics)Selection (genetic algorithm)Scale (ratio)PsychologyArtificial intelligenceComputer scienceCartographyGeographyPhysicsOptics

Abstract

fetched live from OpenAlex

Most children aged five years and older can provide meaningful self-reports of pain intensity if they are provided with age-appropriate tools and training. Self-reports of pain intensity are an oversimplification of the complexity of the experience of pain, but one that is necessary to evaluate and titrate pain-relieving treatments. There are many sources of bias and error in self-reports of pain, so ratings need to be interpreted in light of information from other sources such as direct observation of behaviour, knowledge of the circumstances of the pain and parents' reports. The pain intensity scales most commonly used with children - faces scales, numerical rating scales, visual analogue scales and others - are briefly introduced. The selection, limitations and interpretation of self-report scales are discussed.

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.

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)0.011
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 score0.911
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0110.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.055
GPT teacher head0.368
Teacher spread0.312 · 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