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Record W3187991163 · doi:10.1159/000516972

Bilateral Parietal Skull Fractures in Infants Attributable to Accidental Falls

2021· article· en· W3187991163 on OpenAlexaff
Aysha Alsahlawi, Gillian Morantz, Caroline Lacroix, Christine Saint‐Martin, Roy Dudley

Bibliographic record

VenuePediatric Neurosurgery · 2021
Typearticle
Languageen
FieldMedicine
TopicChild Abuse and Related Trauma
Canadian institutionsMcGill UniversityMontreal Children's Hospital
Fundersnot available
KeywordsMedicineAccidentalSkeletal surveySkullHead traumaPresentation (obstetrics)Poison controlSurgeryHead injuryChild abuseAccidental fallNeuroradiologySkull fractureInjury preventionPediatricsNeurologyMedical emergencyInternal medicine

Abstract

fetched live from OpenAlex

INTRODUCTION: Multiple skull fractures, including bilateral parietal skull fractures (BPSFs) in infants are considered to be suspicious for abusive head trauma (AHT). The aim of this report is to describe a series of BPSF cases in infants which occurred due to accidental falls. METHODS: We searched our neuroradiology database for BPSF in infants (<1 year old) diagnosed between 2006 and 2019; we reviewed initial presentation, mechanisms of injury, clinical course, head imaging, skeletal survey X-rays, ophthalmology, social work and child abuse physicians (CAP) assessments, and long-term follow-up. "Confirmed accidental BPSF" were strictly defined as having negative skeletal survey and ophthalmology evaluation and a CAP conclusion of accidental injury. RESULTS: Twelve cases of BPSF were found; 3 were confirmed to be accidental, with a mean age at presentation of 3 months. Two infants had single-impact falls, and 1 had a compression injury; all 3 had small intracranial hemorrhages. None had bruises or other injuries, and all remained clinically well. A literature search found 10 similar cases and further biomechanical evidence that these fractures can occur from accidental falls. CONCLUSION: While AHT should be kept in the differential diagnosis whenever BPSFs are seen, these injuries can occur as a result of accidental falls.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.819

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.012
GPT teacher head0.271
Teacher spread0.259 · 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.

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

Citations5
Published2021
Admission routes1
Has abstractyes

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