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Record W4408956663 · doi:10.1080/14796694.2025.2481022

Systematic literature review of the epidemiology of neurotrophic tyrosine receptor kinase positive solid tumor sites

2025· review· en· W4408956663 on OpenAlexaboutno aff
Reginald Villacorta, Shannon M. Gallagher‐Colombo, Armin Lahiji, Sky Myers, Jonathon Briggs, Angaja Phalguni

Bibliographic record

VenueFuture Oncology · 2025
Typereview
Languageen
FieldMedicine
TopicLung Cancer Treatments and Mutations
Canadian institutionsnot available
FundersBristol-Myers Squibb
KeywordsMedicineCochrane LibraryInternal medicineMEDLINEOncologyChecklistMeta-analysisIncidence (geometry)Systematic review

Abstract

fetched live from OpenAlex

AIMS: This study aimed to expand on existing systematic literature reviews (SLRs) by assessing the prevalence of neurotrophic tyrosine receptor kinase (NTRK) fusion-positive mutations across solid tumors in adult U.S populations. It further evaluated incidence, testing, treatment, mortality, and progression rates by tumor type, extending evidence through 2023. MATERIALS & METHODS: A SLR was conducted following Cochrane and PRISMA guidelines, with searches across Ovid Embase, Ovid MEDLINE, and Cochrane Library databases for studies published from 2013 to August 2023. Eligibility criteria included studies on NTRK fusion-positive tumors in patients aged ≥12 years. Data were extracted and assessed using the Newcastle-Ottawa Scale and JBI checklist. RESULTS: This SLR identified 160 studies, reporting NTRK fusion prevalence ranging from 0.03% to 0.70% across solid tumors. TRK inhibitors, particularly larotrectinib and entrectinib, were commonly used treatments. Prevalence varied significantly by cancer type, being higher in rarer cancers, such as papillary thyroid carcinoma (up to 21.4%). CONCLUSIONS: NTRK fusions are rare, with wide prevalence variability among cancer types. The findings highlight the need for standardized diagnostic methods and larger real-world studies to improve prevalence estimates and assess the impact of NTRK fusions on outcomes, ultimately aiding in the optimization of targeted treatments for affected patients.

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

Direct model labels (unvalidated)

Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.

Model armCategoriesStudy designConfidence
gemmano category
Domain: not available · Genre: Review
About the Canadian research system: no · About a Canadian topic: no
Systematic reviewhigh
gptno category
Domain: not available · Genre: Review
About the Canadian research system: no · About a Canadian topic: no
Systematic reviewhigh
models agreeAgreement compares identical category sets and study designs across arms.

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)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.247
Threshold uncertainty score0.894

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0060.001
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 head0.023
GPT teacher head0.412
Teacher spread0.389 · 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

Labeled directly by 2 models reading the full record.

The models applied no category: nothing in the taxonomy fit this work.
Study designSystematic review
Domainnot available
GenreReview

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

Citations1
Published2025
Admission routes1
Has abstractyes

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