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Record W4398780340 · doi:10.3389/fonc.2024.1399356

Case report: Slipped capital femoral epiphysis: a rare adverse event associated with FGFR tyrosine kinase inhibitor therapy in a child

2024· article· en· W4398780340 on OpenAlexaff
Meziane Brizini, Tina Drimes, C. Bourne, Jessica Streilein, Annie Drapeau, Jens Wrogemann, Lori Anne Archer, Marc R. Del Bigio, Magimairajan Vanan

Bibliographic record

VenueFrontiers in Oncology · 2024
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicFibroblast Growth Factor Research
Canadian institutionsResearch Institute in Oncology and HematologyUniversity of ManitobaCancerCare Manitoba
Fundersnot available
KeywordsSlipped capital femoral epiphysisMedicineDiscontinuationAdverse effectShunt (medical)SurgeryInternal medicineFemoral head

Abstract

fetched live from OpenAlex

We report a case of slipped capital femoral epiphysis (SCFE), an on target skeletal toxicity of a pan-FGFR TKI inhibitor, erdafitinib. A 13-year-old boy was diagnosed to have an optic pathway/hypothalamic glioma with signs of increased intracranial pressure and obstructive hydrocephalus requiring placement of ventriculo-peritoneal (VP) shunt. Sequencing of the tumor showed FGFR1-tyrosine kinase domain internal tandem duplication (FGFR1-KD-ITD). He developed hypothalamic obesity with rapid weight gain and BMI >30. At 12 weeks of treatment with erdafitinib, he developed persistent knee pain. X-ray of the right hip showed SCFE. Erdafitinib was discontinued, and he underwent surgical pinning of the right hip. MRI at discontinuation of erdafitinib showed a 30% decrease in the size of the tumor, which has remained stable at 6 months follow-up. Our experience and literature review suggest that pediatric patients who are treated with pan-FGFR TKIs should be regularly monitored for skeletal side effects.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Case report · Consensus signal: Case report
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.003
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0020.002
Scholarly communication0.0010.002
Open science0.0010.002
Research integrity0.0050.003
Insufficient payload (model declined to judge)0.0020.001

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 head0.010
GPT teacher head0.276
Teacher spread0.267 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designCase report
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 routes1
Has abstractyes

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