Evidence map›Paper›PMID 41936683›Full record

ArticleAnnals of surgical oncology2026

Uncovering Global Trends in Surgical Oncology Trials: Applying Natural Language Processing to ClinicalTrials.gov.

Hasnaien Ahmed, Zofia Czarnecka, Sukhdeep Jatana, Armin Rouhi, Chris Brunet, Asher Khan, Safaa Bashir, Michael McCall, Abdullah Saleh, Daniel Schiller and 1 more

Abstract read
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In one paragraph

Article in Annals of surgical oncology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

11 authors.

Hasnaien AhmedDepartment of Surgery, College of Health Sciences, University of Alberta, Edmonton, AB, Canada.
Zofia CzarneckaDepartment of Surgery, College of Health Sciences, University of Alberta, Edmonton, AB, Canada.
Sukhdeep JatanaDepartment of Surgery, College of Health Sciences, University of Alberta, Edmonton, AB, Canada.
Armin RouhiDepartment of Surgery, College of Health Sciences, University of Alberta, Edmonton, AB, Canada.
Chris BrunetFaculty of Medicine and Dentistry, College of Health Sciences, University of Alberta, Edmonton, Canada.
Asher KhanFaculty of Medicine and Dentistry, College of Health Sciences, University of Alberta, Edmonton, Canada.
Safaa BashirFaculty of Medicine and Dentistry, College of Health Sciences, University of Alberta, Edmonton, Canada.
Michael McCallDepartment of Surgery, College of Health Sciences, University of Alberta, Edmonton, AB, Canada.
Abdullah SalehDepartment of Surgery, College of Health Sciences, University of Alberta, Edmonton, AB, Canada.
Daniel SchillerDepartment of Surgery, College of Health Sciences, University of Alberta, Edmonton, AB, Canada.
Daniel SkublenyDepartment of Surgery, College of Health Sciences, University of Alberta, Edmonton, AB, Canada. skubleny@ualberta.ca.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundIdentifying surgical oncology trials within the National Clinical Trial (NCT) database is challenging owing to the absence of medical specialty labels. We developed a machine learning classifier to systematically identify and analyze surgical oncology trials registered in the NCT database. MATERIALS AND

methodsWe analyzed 425,736 trials. A training dataset of 4863 trials were labeled as surgical oncology according to Complex General Surgical Oncology (CGSO) neoplasms. Labels were applied by two independent reviewers, and disagreements resolved by a third, using study descriptions and Medical Subject Headings (MeSH). Neural networks were assessed using stratified 5-fold cross-validation. Models were evaluated using accuracy, balanced accuracy, specificity, sensitivity, and Cohen's kappa. Neural embedding representations were analyzed with principal component analysis (PCA). Trial characteristics, geospatial mapping and social network analyses were evaluated.

resultsThe selected model achieved excellent accuracy (94.1% [95%CI 93.3-94.9]). PCA of neural embeddings demonstrated separation of CGSO-relevant terms. We identified 24,345 surgical oncology trials. Trial volume was strongly associated with national GDP (r = 0.96) and global disparities were identified. Network analysis revealed strong collaboration amongst USA and European institutions. Chinese institutions demonstrated high trial leadership but relatively limited external collaboration. Academic centers including Memorial Sloan Kettering and MD Anderson remained prominent, while industry participation, was consistently evident.

conclusionsThis study provides a machine learning approach to identify surgical oncology trials within the NCT database. Our research highlights collaboration networks amongst institutions and global disparities in surgical oncology research. Future work should incorporate funding information to better inform surgical research policy and development.

Indexed as

Clinical Trials as TopicDatabases, FactualMachine LearningNatural Language ProcessingNeoplasmsSurgical OncologyClassification AlgorithmsHumansNeural Networks, ComputerClinical trialGlobal surgeryMachine learningNatural language processingOncologyRandomized control trialSurgerySurgical oncology

Identifiers

PMID41936683

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.