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Low-Value Clinical Practices in Pediatric Trauma Care

2024· article· en· W4403852213 on OpenAlexafffundabout
Theony Deshommes, Gabrielle Freire, Natalie Yanchar, Roger Zemek, Marianne Beaudin, Antonia Stang, Matthew J. Weiss, Sasha Carsen, Isabelle Gagnon, Belinda J. Gabbe, Mélanie Berube, Henry T. Stelfox, Suzanne Beno, Mélanie Labrosse, Émilie Beaulieu, Simon Berthelot, Terry P. Klassen, Alexis F. Turgeon, François Lauzier, Xavier Neveu, Amina Belcaïd, Anis Ben Abdeljelil, Pier‐Alexandre Tardif, Marianne Giroux, Lynne Moore

Bibliographic record

VenueJAMA Network Open · 2024
Typearticle
Languageen
FieldHealth Professions
TopicHealthcare cost, quality, practices
Canadian institutionsInstitut National d'Excellence en Santé et en Services SociauxChildren's Hospital Research Institute of ManitobaUniversity of TorontoMontreal Children's HospitalCentre Hospitalier Universitaire Sainte-JustineChildren's Hospital of Eastern OntarioUniversity of CalgaryMcGill UniversityMcGill University Health CentreInstitute for Clinical Evaluative SciencesSickKids FoundationHospital for Sick ChildrenUniversité de MontréalUniversité LavalUniversity of ManitobaHôpital de l'Enfant-Jésus
FundersCanadian Institutes of Health ResearchOntario Neurotrauma FoundationUniversity of OttawaPublic Health AgencyPublic Health Agency of CanadaHealth Canada
KeywordsMedicineIncidence (geometry)Emergency medicineRetrospective cohort studyDeep veinPediatricsThrombosisInternal medicine

Abstract

fetched live from OpenAlex

Importance: Reducing low-value care has the potential to improve patient experiences and outcomes and decrease the unnecessary use of health care resources. Research suggests that low-value practices (ie, the potential for harm exceeds the potential for benefit) in adult trauma care are frequent and subject to interhospital variation; evidence on low-value practices in pediatric trauma care is lacking. Objective: To estimate the incidence of low-value practices in pediatric trauma care and evaluate interhospital practice variation. Design, Setting, and Participants: A retrospective multicenter cohort study in a Canadian provincial trauma system was conducted. Children younger than 16 years admitted to any of the 59 provincial trauma centers from April 1, 2016, to March 31, 2022, were included. Main Outcomes and Measures: Low-value practices were identified from systematic reviews of clinical practice guidelines on pediatric trauma. The frequencies of low-value practices were evaluated by estimating incidence proportions and cases per 1000 admissions (low if ≤10% and ≤10 cases, moderate if >10% or >10 cases, and high if >10% and >10 cases) were identified. Interhospital variation with intraclass correlation coefficients (ICCs) were assessed (low if <5%, moderate if 5%-20%, and high if >20%). Results: A total of 10 711 children were included (mean [SD] age, 7.4 [4.9] years; 6645 [62%] boys). Nineteen low-value practices on imaging, fluid resuscitation, hospital/intensive care unit admission, specialist consultation, deep vein thrombosis prophylaxis, and surgical management of solid organ injuries were identified. Of these, 14 (74%) could be evaluated using trauma registry data. Five practices had moderate to high frequencies and interhospital variation: head computed tomography in low-risk children (7.1%; 33 per 1000 admissions; ICC, 8.6%), pretransfer computed tomography in children with a clear indication for transfer (67.6%; 4 per 1000 admissions; ICC, 5.7%), neurosurgical consultation in children without clinically important intracranial lesions (11.6%; 13 per 1000 admissions; ICC, 15.8%), hospital admission in isolated mild traumatic brain injury (38.8%; 98 per 1000 admissions; ICC, 12.4%), and hospital admission in isolated minor blunt abdominal trauma (10%; 5 per 1000 admissions; ICC, 31%). Conclusions and Relevance: In this cohort study, low-value practices appeared to be frequent and subject to interhospital variation. These practices may represent priority targets for deimplementation interventions, particularly as they can be measured using routinely collected data.

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

Direct model labels (unvalidated)

Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.

Model armCategoriesStudy designConfidence
gemmano category
Domain: not available · Genre: Empirical
About the Canadian research system: no · About a Canadian topic: no
Observationallow
gptno category
Domain: not available · Genre: Empirical
About the Canadian research system: no · About a Canadian topic: no
Observationallow
models agreeAgreement compares identical category sets and study designs across arms.

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)0.024
metaresearch head score (Gemma)0.006
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.379
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0240.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.002
Open science0.0010.001
Research integrity0.0010.005
Insufficient payload (model declined to judge)0.0010.004

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 head0.685
GPT teacher head0.641
Teacher spread0.045 · 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

Labeled directly by 2 models reading the full record.

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

Citations4
Published2024
Admission routes3
Has abstractyes

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