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Record W2230369686 · doi:10.1093/pch/19.4.190

Paediatric pain management practice and policies across Alberta emergency departments

2014· article· en· W2230369686 on OpenAlexaffabout
Samina Ali, Andrea Chambers, David W. Johnson, William Craig, Amanda S. Newton, Ben Vandermeer, Sarah Curtis

Bibliographic record

VenuePaediatrics & Child Health · 2014
Typearticle
Languageen
FieldMedicine
TopicPediatric Pain Management Techniques
Canadian institutionsAlberta Children's HospitalUniversity of CalgaryWomen and Children’s Health Research InstituteUniversity of Alberta
FundersAstraZeneca
KeywordsPain managementEmergency managementMedicineEmergency departmentPractice managementMedical emergencyBusinessFamily medicineNursingPolitical sciencePhysical therapy

Abstract

fetched live from OpenAlex

BACKGROUND: Many children requiring acute care receive suboptimal analgesia. OBJECTIVES: To describe paediatric pain management practices and policies in emergency departments (EDs) in Alberta. METHODS: A descriptive survey was distributed to each of the EDs in Alberta. RESULTS: A response rate of 67% (72 of 108) was obtained. Seventy-one percent (42 of 59) of EDs reported the use of a pain tool, 29.3% (17 of 58) reported mandatory pain documentation and 16.7% (10 of 60) had nurse-initiated pain protocols. Topical anesthetics were reported to be used for intravenous line insertion by 70.4% of respondents (38 of 54) and for lumbar puncture (LP) by 30.8% (12 of 39). According to respondents, infiltrated anesthetic was used for LP by 69.2% (27 of 39) of respondents, and oral sucrose was used infrequently for urinary catheterization (one of 46 [2.2%]), intravenous line insertion (zero of 54 [0%]) and LP (one of 39 [2.6%]). CONCLUSIONS: Few Alberta EDs use policies and protocols to manage paediatric pain. Noninvasive methods to limit procedural pain are underutilized. Canadian paediatricians must advocate for improved analgesia to narrow this knowledge-to-practice gap.

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)0.006
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.631
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
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 head0.011
GPT teacher head0.318
Teacher spread0.307 · 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 designObservational
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

Citations38
Published2014
Admission routes2
Has abstractyes

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