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Record W2011133026 · doi:10.1097/paf.0b013e31828c38f9

Retinal Hemorrhage After Cardiopulmonary Resuscitation With Chest Compressions

2013· article· en· W2011133026 on OpenAlexfundno aff
Hang Pham, Robert W. Enzenauer, James E. Elder, Alex V. Levin

Bibliographic record

VenueAmerican Journal of Forensic Medicine & Pathology · 2013
Typearticle
Languageen
FieldMedicine
TopicChild Abuse and Related Trauma
Canadian institutionsnot available
FundersHospital for Sick Children
KeywordsMedicineCardiopulmonary resuscitationRetinalPathognomonicResuscitationRetinopathySurgeryOphthalmologyInternal medicineDisease

Abstract

fetched live from OpenAlex

Retinal hemorrhages in children in the absence of risk factors are regarded to be pathognomonic of shaken baby syndrome or other nonaccidental injuries. The physician must decide whether the retinal hemorrhages in children without risk factors are due to abuse or cardiopulmonary resuscitation with chest compression (CPR-CC). The objective of this study was to determine if CPR-CC can lead to retinal hemorrhages in children. Twenty-two patients who received in-hospital CPR-CC between February 15, 1990, and June 15, 1990, were enrolled. Pediatric ophthalmology fellows carried a code beeper and responded to calls for cardiopulmonary arrest situations. At the scene of CPR-CC, an indirect funduscopic examination was conducted for presence of retinal hemorrhages in the posterior pole. Follow-up examinations were performed at 24 and 72 hours. Of the 22 patients, 6 (27%) had retinal hemorrhages at the time of CPR-CC. Of these 6 patients, 5 had risk factors for retinal hemorrhages. The sixth patient had no risk factors and may have represented the only true case of retinal hemorrhages due to CPR-CC. Retinal hemorrhages are uncommon findings after CPR-CC. Retinal hemorrhages that are found after CPR-CC usually occur in the presence of other risk factors for hemorrhage with a mild hemorrhagic retinopathy in the posterior pole.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.683
Threshold uncertainty score0.521

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.008
GPT teacher head0.242
Teacher spread0.234 · 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

Citations26
Published2013
Admission routes1
Has abstractyes

Explore more

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