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Record W3105827283 · doi:10.1001/jamasurg.2020.5045

Guidelines for Opioid Prescribing in Children and Adolescents After Surgery

2020· review· en· W3105827283 on OpenAlexaff
Lorraine I. Kelley‐Quon, Matthew G. Kirkpatrick, Robert L. Ricca, Robert Baird, Calista M. Harbaugh, Ashley E. Brady, Paula Garrett, Hale Wills, Jonathan Argo, Karen A. Diefenbach, Marion C.W. Henry, Juan E. Sola, Elaa Mahdi, Adam B. Goldin, Shawn D. St. Peter, Cynthia D. Downard, Kenneth S. Azarow, Tracy Shields, Eugene Kim

Bibliographic record

VenueJAMA Surgery · 2020
Typereview
Languageen
FieldMedicine
TopicPediatric Pain Management Techniques
Canadian institutionsBC Children's HospitalUniversity of British Columbia
FundersNational Center for Advancing Translational Sciences
KeywordsMedicineCINAHLMEDLINEGuidelineFamily medicineMedical prescriptionPediatric surgeryHealth careCochrane LibraryMultidisciplinary approachEvidence-based medicinePediatricsAlternative medicinePsychiatryNursingPsychological intervention

Abstract

fetched live from OpenAlex

Importance: Opioids are frequently prescribed to children and adolescents after surgery. Prescription opioid misuse is associated with high-risk behavior in youth. Evidence-based guidelines for opioid prescribing practices in children are lacking. Objective: To assemble a multidisciplinary team of health care experts and leaders in opioid stewardship, review current literature regarding opioid use and risks unique to pediatric populations, and develop a broad framework for evidence-based opioid prescribing guidelines for children who require surgery. Evidence Review: Reviews of relevant literature were performed including all English-language articles published from January 1, 1988, to February 28, 2019, found via searches of the PubMed (MEDLINE), CINAHL, Embase, and Cochrane databases. Pediatric was defined as children younger than 18 years. Animal and experimental studies, case reports, review articles, and editorials were excluded. Selected articles were graded using tools from the Oxford Centre for Evidence-based Medicine 2011 levels of evidence. The Appraisal of Guidelines for Research & Evaluation (AGREE) II instrument was applied throughout guideline creation. Consensus was determined using a modified Delphi technique. Findings: Overall, 14 574 articles were screened for inclusion, with 217 unique articles included for qualitative synthesis. Twenty guideline statements were generated from a 2-day in-person meeting and subsequently reviewed, edited, and endorsed externally by pediatric surgical specialists, the American Pediatric Surgery Association Board of Governors, the American Academy of Pediatrics Section on Surgery Executive Committee, and the American College of Surgeons Board of Regents. Review of the literature and guideline statements underscored 3 primary themes: (1) health care professionals caring for children who require surgery must recognize the risks of opioid misuse associated with prescription opioids, (2) nonopioid analgesic use should be optimized in the perioperative period, and (3) patient and family education regarding perioperative pain management and safe opioid use practices must occur both before and after surgery. Conclusions and Relevance: These are the first opioid-prescribing guidelines to address the unique needs of children who require surgery. Health care professionals caring for children and adolescents in the perioperative period should optimize pain management and minimize risks associated with opioid use by engaging patients and families in opioid stewardship efforts.

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)0.012
metaresearch head score (Gemma)0.054
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: Not applicable
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.013
Threshold uncertainty score0.064

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0120.054
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0050.005
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0030.002
Research integrity0.0050.005
Insufficient payload (model declined to judge)0.0050.003

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 head0.082
GPT teacher head0.347
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 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

Citations166
Published2020
Admission routes1
Has abstractyes

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