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Record W1984882725 · doi:10.1111/apa.12797

The Pediatric Emergency Care Applied Research Network intermediate-risk predictors were not associated with scanning decisions for minor head injuries

2014· article· en· W1984882725 on OpenAlexaff
Silvia Bressan, Ivan P. Steiner, Teresa Mion, Paola Berlese, Sabrina Romanato, Liviana Da Dalt

Bibliographic record

VenueActa Paediatrica · 2014
Typearticle
Languageen
FieldMedicine
TopicTraumatic Brain Injury and Neurovascular Disturbances
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsMedicineComputed tomographyNeurosurgeryEmergency departmentHead injuryEmergency medicinePediatricsRadiologySurgeryPsychiatry

Abstract

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AIM: This study determined the predictors associated with the decision to perform a computed tomography (CT) scan in children with a minor head injury (MHI). We focused on those facing an intermediate risk of clinically important traumatic brain injury (ciTBI), according to the Pediatric Emergency Care Applied Research Network (PECARN) prediction rule. METHODS: A 1-year, cross-sectional study was performed in an Italian paediatric emergency department, focusing on children presenting within 24 h of an MHI and meeting the PECARN intermediate-risk criteria. RESULTS: We included 308 children, and 47% were younger than 2 years of age. CT scans were carried out on 13%, 1.3% had a ciTBI and one was initially missed but did not need neurosurgery following diagnosis. Single and multiple PECARN intermediate-risk predictors were not associated with whether a CT scan was carried out. The only clinical variable associated with the decision to perform a CT scan was if the child was <3 months of age (OR 18.1, 95% CI, 4.91-66.61). CONCLUSION: The PECARN intermediate-risk predictors did not play a major role in the decision to perform a CT scan. The only factor significantly associated with the decision to perform a CT scan was when the patient was younger than 3 months of age.

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)0.002
metaresearch head score (Gemma)0.006
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.140
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0020.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.035
GPT teacher head0.310
Teacher spread0.275 · 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

Citations13
Published2014
Admission routes1
Has abstractyes

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