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Record W4414413529 · doi:10.1007/s10143-025-03771-z

Medical student perceptions and career intentions toward neurosurgery: results from an international multicentre study

2025· article· en· W4414413529 on OpenAlexaff
Mohammad Ashraf, Hassan Ismahel, Gemma Learmonth, Aneesah Bashir Binti Azad Bashir, Conor S. Gillespie, Mohammad Alabdulrahman, Attika Chaudhary, Ameerah Gardee, Devansh Mitesh Shah, Abdel Rahman Osman, Ahmed Abdelfattah, Beth Behan, Brian Mc Cormick, Bryan Way Wern Lim, Caoilfhinn Tan, Dana Hutton, Daniel Sescu, Emily Boyd, Hamzah S. Hanif, Jane Birdie Shi Qi Ong, Kelly Kohut, Maha Shehab, Vidushi Sharma, Yen-Yi Ho, Zara Tebay, Nadeen Ismahel, S. Salem, Sophia Ismahel, Victoria Collins, Javed Iqbal, Samih Hassan, Roddy O’Kane, Naveed Ashraf, Laulwa Al Salloum, Yihui Cheng, Emma Ball, Sophie Hay, Gregory Kosisochukwu Anyaegbunam, Vivienne Evans, Eilidh Middleton, Meghan Minnis, Ranoo Rebaz, Robert Lawless, Aakansha Singh, Natalie Ong, H.L. Brown, Andrew Baird, Ali Hamad, M P O Connell, Haneesha Danduri, Sujashree Yadala Venkata, Rory Anderson, Alexander Tham, E Carberry, Lynden Guy Nicely, Amirah Amzizul, Poppy Wright, Catherine Kenneth-Ogah, M. Pearson, Rui Na, Nicola Bakirtzi, Emma Lumsden, Owen Gray, Charlotte Williams, Cathy Wang, Louise Cassidy, Daniel Patel, Irfaan Ahmmed, Humzah Razzaq, F. C. Heath, Carolyn Eyster Thomas, Danielle M. Robertson, Inas Alsuhaibani, J Luangboriboon, Emma C. Thomson, Emma MacRae, Freda Ngu, A Irving, Agha Haider, Amanpreet Kaur, Megan Niven, Natthaya Eiamampai, Shazia Syeda Nusky, Onamon Jaruwattanapradid, Hanah Abdel Fattah, Jennifer McLair, Sze May Ng, Naveen Kaur Rikhraj, Abdulaziz M. Al-Othman, Sofia Kostoudi, Matilda Brown, Srisha Dristi, Ammee Gala, Nimue Lilith Romeikat, Lucie Destexhe, Sushmhitah Sandanatavan, Fionn Kilmartin, James A. Kenny, Mahmoud Al-Ghabari, Scotti Leonard, Arielle Muyal, Tala Al-Qaragolie, Maryam Albreiki, Aubrie Sowa, Aditya Billur, Gabrielle Morewood, Alixandra Lammie, Doris Braunstein, M. Al Kurdi, Arshia Bilal, Saketh Jampana, Chris Happs, Julia Anderson, Thomas Fawcett, William Cawley, Jack Baker, J. Fowler, Angus Reid, Л. К. Эрнст, Alim Kapdi, Aashik Ahamed Mohamed Jemseed, Heather McAdam, Vito Balboa, Ellie Costelloe, Daniel O. Griffin, Paddy Geohean, C. A. Ryan, Sophie McCarthy, Yvonne Buttle, Cliona Nic Giolla Phadraig, Aislinn Cosgrave, Ruán Ó Conluain, Rayan Zaibag, Eoghan O Callahan, Freya O’Hanlon, L. Yun, Yasmina Richa, John Begley, Muhammad Irfan, Ryan Conchobhar, Ekanki Chawla, Hannah Whibbs, Hannah O’ Connor, Katherine Jones, Yukta Ramesh, Joey Harrinton, Andrew Iskander, Dante Bellai, Benjamin Olden, R. Baker, Martin Ho, Ashwini Tittawella, Tulika Nahar, Eleni Flari, Kirsty Macmillan, Sarindie Katugaha, Beth McCullough, Ariana Axiaq, F. S. Beers, Callum Blair, Faye Mui, Inez Murray, Sakshi Roy, Swati Joshi, Grace Ross, Naveen Kulasingha, Elizabeth Gorecki, Nicholas Stamatopoulos, Lea Stuart, Lina Adil, Kurdo Araz, Sonali Loomba, Maureen Graham, Christina Adesanya, Harpriya Khela, Jawad Al-Kassmy, Natalie Lane, Alexandra De Sequeira, Alyssa N. Clark, Walid Nabilsi, Maie Tali, Harsimran Kaur, Thomas Kearney, Steven Browne, Philip Howard, Abdel Rahman Salameh, Vidhi Patel, Michael O′Connor, Cillian Scott, Senara Palihawadane, J. Tan, Harn Yeap, Alfredi Mulihano, Samantha Nalliah, Pei Nie Ho, Yan Yi Sin, Eunice Young

Bibliographic record

VenueNeurosurgical Review · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicDiversity and Career in Medicine
Canadian institutionsTrinity College
Fundersnot available
KeywordsNeurosurgeryThematic analysisPerceptionIrishQualitative researchMedical school

Abstract

fetched live from OpenAlex
No abstract in any covered source. Its absence is recorded, not treated as a negative.

No abstract. This is not a gap in this database; OpenAlex has none either. 23.3% of the frame is in this state, and the screen finds HALF as much metaresearch here, so the absence is a measured bias rather than a missing field.

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)0.003
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Incentives · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.997
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.003
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.059
GPT teacher head0.406
Teacher spread0.346 · 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.

Study designObservational
DomainIncentives
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 abstractno

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