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Record W2741094981 · doi:10.1097/mpg.0000000000001701

Radiation Exposure and Attributable Cancer Risk in Patients With Esophageal Atresia

2017· article· en· W2741094981 on OpenAlexaff
Yasmine Yousef, Robert Baird

Bibliographic record

VenueJournal of Pediatric Gastroenterology and Nutrition · 2017
Typearticle
Languageen
FieldMedicine
TopicEsophageal and GI Pathology
Canadian institutionsMcGill UniversityMontreal Children's Hospital
Fundersnot available
KeywordsMedicineAtresiaEsophageal cancerRadiation exposureRisk factorCancerInternal medicineNuclear medicine

Abstract

fetched live from OpenAlex

OBJECTIVES: Cases of esophageal carcinoma have been documented in survivors of esophageal atresia (EA). Children with EA undergo considerable amounts of diagnostic imaging and consequent radiation exposure potentially increasing their lifetime cancer mortality risk. This study evaluates the radiological procedures performed on patients with EA and estimates their cumulative radiation exposure and attributable lifetime cancer mortality risk. METHODS: Medical records of patients with EA managed at a tertiary care center were reviewed for demographics, EA subtype, and number and type of radiological investigations. Existing normative data were used to estimate the cumulative radiation exposure and lifetime cancer risk per patient. RESULTS: The present study included 53 patients with a mean follow-up of 5.7 years. The overall median and maximum estimated effective radiation dose in the neonatal period was 5521.4 μSv/patient and 66638.6 μSv/patient, respectively. This correlates to a median and maximum estimated cumulative lifetime cancer mortality risk of 1:1530 and 1:130, respectively. Hence, radiation exposure in the neonatal period increased the cumulative cancer mortality risk a median of 130-fold and a maximum of 1575-fold in EA survivors. CONCLUSIONS: Children with EA are exposed to significant amounts of radiation and an increased estimated cumulative cancer mortality risk. Efforts should be made to eliminate superfluous imaging.

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 score0.013
Threshold uncertainty score0.276

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.006
GPT teacher head0.238
Teacher spread0.232 · 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

Citations9
Published2017
Admission routes1
Has abstractyes

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