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Record W2121774745 · doi:10.1177/0009922814555972

Acute Pediatric Musculoskeletal Pain Management in North America

2014· article· en· W2121774745 on OpenAlexafffund
Janeva Kircher, Amy L. Drendel, Amanda S. Newton, Amy C. Plint, Ben Vandermeer, Sukhdeep Dulai, Samina Ali

Bibliographic record

VenueClinical Pediatrics · 2014
Typearticle
Languageen
FieldMedicine
TopicPediatric Pain Management Techniques
Canadian institutionsUniversity of OttawaWomen and Children’s Health Research InstituteUniversity of Alberta
FundersCanadian Institutes of Health Research
KeywordsMedicineIbuprofenAcetaminophenEmergency departmentCodeineAnalgesicOrthopedic surgeryOpioidPain managementAcute painEmergency medicineAnesthesiaPhysical therapyInternal medicineSurgeryMorphinePsychiatry

Abstract

fetched live from OpenAlex

Children's musculoskeletal (MSK) injury pain remains poorly managed. This survey of pediatric emergency physicians and orthopedic surgeons assessed analgesia administration practices and discharge advice for children with acute MSK pain; 683 responses were received. Ibuprofen was the most commonly reported analgesic used in the emergency department (52%) and at discharge (68%). Most (85%) reported using oral opioids in the previous 6 months. Codeine use was the most commonly reported opioid used in the emergency department (38%) and at home (51%). For equal levels of pain, younger children received less opioids than older children. Younger physicians and recent graduates chose acetaminophen and codeine more than older and more experienced colleagues, who preferred ibuprofen and non-codeine containing opioid compounds (P < .001 and .006, respectively). Orthopedic surgeons reported less ibuprofen use than pediatric emergency physicians (P < .001). Choice of analgesic agents is heterogeneous among physicians and is influenced by pain severity, child's age, and physician characteristics.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: none
Teacher disagreement score0.029
Threshold uncertainty score0.058

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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)0.0030.000

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 head0.021
GPT teacher head0.347
Teacher spread0.326 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations31
Published2014
Admission routes2
Has abstractyes

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