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Record W2143281062 · doi:10.3138/physio.62.4.327

Procedural Pain Management for Children Receiving Physiotherapy

2010· article· en· W2143281062 on OpenAlexaffvenue
Carl L. von Baeyer, Susan Tupper

Bibliographic record

VenuePhysiotherapy Canada · 2010
Typearticle
Languageen
FieldMedicine
TopicPediatric Pain Management Techniques
Canadian institutionsUniversity of SaskatchewanSaskatchewan HealthSaskatchewan Health Authority
Fundersnot available
KeywordsMedicinePhysical therapyDistressMEDLINEHealth carePain managementPhysical medicine and rehabilitationClinical psychology

Abstract

fetched live from OpenAlex

PURPOSE: This article provides an overview of literature relevant to the prevention and relief of pain and distress during physiotherapy procedures, with guidance for physiotherapists treating children. SUMMARY OF KEY POINTS: Physiotherapists are generally well trained in assessing and managing pain as a symptom of injury or disease, but there is a need to improve the identification and management of pain produced by physiotherapy procedures such as stretching and splinting. In contrast to physiotherapy, other health care disciplines, such as dentistry, nursing, paediatrics, emergency medicine, and paediatric psychology, produce extensive literature on painful procedures. Procedural pain in children is particularly important because it can lead to later fear and avoidance of necessary medical care. RECOMMENDATIONS: We emphasize the need for physiotherapists to recognize procedural pain and fear in the course of treatment using verbal, nonverbal, and contextual cues. We present many methods that physiotherapists can use to prevent or relieve procedural pain and fear in paediatric patients and provide an example of a simple, integrated plan for prevention and relief of distress induced by painful procedures.

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)0.000
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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.656
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.005
GPT teacher head0.269
Teacher spread0.265 · 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
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

Citations25
Published2010
Admission routes2
Has abstractyes

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