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Centers for Disease Control and Prevention Guideline on the Diagnosis and Management of Mild Traumatic Brain Injury Among Children

2018· article· en· W2890806027 on OpenAlexaff
Angela Lumba‐Brown, Keith Owen Yeates, Kelly Sarmiento, Matthew J. Breiding, Tamara M. Haegerich, Gérard A. Gioia, Michael S. Turner, Edward C. Benzel, Stacy J. Suskauer, Christopher C. Giza, Madeline Joseph, Catherine Broomand, Barbara Weissman, Wayne A. Gordon, David W. Wright, Rosemarie Scolaro Moser, Karen McAvoy, Linda Ewing‐Cobbs, Ann-Christine Duhaime, Margot Putukian, Barbara A. Holshouser, David Paulk, Shari L. Wade, Stanley A. Herring, Mark Halstead, Heather T. Keenan, Meeryo Choe, Cindy W. Christian, Kevin M. Guskiewicz, P B Raksin, Andrew Gregory, Anne Mucha, H. Gerry Taylor, James Callahan, John DeWitt, Michael W. Collins, Michael W. Kirkwood, John Ragheb, Richard G. Ellenbogen, Theodore J. Spinks, Théodore G. Ganiats, Linda J. Sabelhaus, Katrina Altenhofen, Rosanne Hoffman, Tom Getchius, Gary Gronseth, Zoe Donnell, Robert E. O’Connor, Shelly D. Timmons

Bibliographic record

VenueJAMA Pediatrics · 2018
Typearticle
Languageen
FieldMedicine
TopicTraumatic Brain Injury Research
Canadian institutionsUniversity of Calgary
FundersNational Institutes of Health
KeywordsWorkgroupMedicineGuidelineTraumatic brain injuryConcussionMEDLINEFamily medicinePoison controlInjury preventionEmergency departmentEvidence-based medicineMedical emergencyPsychiatryAlternative medicinePathology

Abstract

fetched live from OpenAlex

Importance: Mild traumatic brain injury (mTBI), or concussion, in children is a rapidly growing public health concern because epidemiologic data indicate a marked increase in the number of emergency department visits for mTBI over the past decade. However, no evidence-based clinical guidelines have been developed to date for diagnosing and managing pediatric mTBI in the United States. Objective: To provide a guideline based on a previous systematic review of the literature to obtain and assess evidence toward developing clinical recommendations for health care professionals related to the diagnosis, prognosis, and management/treatment of pediatric mTBI. Evidence Review: The Centers for Disease Control and Prevention (CDC) National Center for Injury Prevention and Control Board of Scientific Counselors, a federal advisory committee, established the Pediatric Mild Traumatic Brain Injury Guideline Workgroup. The workgroup drafted recommendations based on the evidence that was obtained and assessed within the systematic review, as well as related evidence, scientific principles, and expert inference. This information includes selected studies published since the evidence review was conducted that were deemed by the workgroup to be relevant to the recommendations. The dates of the initial literature search were January 1, 1990, to November 30, 2012, and the dates of the updated literature search were December 1, 2012, to July 31, 2015. Findings: The CDC guideline includes 19 sets of recommendations on the diagnosis, prognosis, and management/treatment of pediatric mTBI that were assigned a level of obligation (ie, must, should, or may) based on confidence in the evidence. Recommendations address imaging, symptom scales, cognitive testing, and standardized assessment for diagnosis; history and risk factor assessment, monitoring, and counseling for prognosis; and patient/family education, rest, support, return to school, and symptom management for treatment. Conclusions and Relevance: This guideline identifies the best practices for mTBI based on the current evidence; updates should be made as the body of evidence grows. In addition to the development of the guideline, CDC has created user-friendly guideline implementation materials that are concise and actionable. Evaluation of the guideline and implementation materials is crucial in understanding the influence of the recommendations.

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)0.023
metaresearch head score (Gemma)0.102
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: Other · Consensus signal: none
Teacher disagreement score0.075
Threshold uncertainty score0.149

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0230.102
Meta-epidemiology (narrow)0.0020.002
Meta-epidemiology (broad)0.0050.008
Bibliometrics0.0090.008
Science and technology studies0.0020.002
Scholarly communication0.0040.003
Open science0.0080.003
Research integrity0.0110.014
Insufficient payload (model declined to judge)0.0090.006

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 head0.057
GPT teacher head0.356
Teacher spread0.299 · 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
GenreOther

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

Citations537
Published2018
Admission routes1
Has abstractyes

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