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Record W4413048394 · doi:10.1007/s00259-025-07499-2

A century in the making: Medical imaging, nuclear medicine, and the transformation of global healthcare

2025· article· en· W4413048394 on OpenAlexaff
Humayun Bashir, Stefano Fanti

Bibliographic record

VenueEuropean Journal of Nuclear Medicine and Molecular Imaging · 2025
Typearticle
Languageen
FieldMedicine
TopicGlobal Health and Surgery
Canadian institutionsCarleton University
Fundersnot available
KeywordsCredentialingHealth careMedical imagingAccountabilityNuclear medicine imagingMedicineBusinessPublic relationsPolitical scienceEngineering ethicsNuclear medicineMedical educationRadiologyEngineeringLaw

Abstract

fetched live from OpenAlex

This paper explores how the global mobility of healthcare professionals shapes the evolution of nuclear medicine through shifts in labour, logistics, and knowledge systems. It examines how credentialing asymmetries, fragile supply infrastructures, and uneven digital integration affect equitable access to innovation and practice. The analysis highlights how professional circulation generates both collaboration and structural exclusion, particularly when expertise crosses regulatory and institutional boundaries. It argues that mobility not only redistributes clinical skills but also redefines authority, standards, and epistemologies in medical imaging. By tracing these dynamics, the paper opens space for rethinking inclusion, accountability, and fairness in the global development of medical imaging and nuclear medicine.

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)0.008
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.016
Threshold uncertainty score0.045

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.003
Science and technology studies0.0070.043
Scholarly communication0.0160.019
Open science0.0010.011
Research integrity0.0060.007
Insufficient payload (model declined to judge)0.0070.001

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 head0.011
GPT teacher head0.304
Teacher spread0.294 · 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 designNot applicable
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

Citations1
Published2025
Admission routes1
Has abstractyes

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