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Untold story of an intercontinental pioneering neurosurgeon: vahdettin turkman, m.d.

2023· article· en· W4317105929 on OpenAlexaboutno aff
Eyüp Bayatli, Selçuk Palaoğlu

Bibliographic record

VenueTurkish Neurosurgery · 2023
Typearticle
Languageen
FieldMedicine
TopicHistory of Medical Practice
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineHonorNeurosurgeryTributeFamily medicineLawPsychiatryPolitical science

Abstract

fetched live from OpenAlex

AIM: To present one of neurosurgery's earliest pioneers, Dr. Vahdettin Turkman, who contributed to neurosurgical practice globally from east to west (Iraq, Tukey, England, Germany and the United States) in the early 1960s. MATERIAL AND METHODS: This paper is the result of numerous interviews conducted in Turkey, Iraq, USA, and Canada. RESULTS: During Dr. Turkman's brief life, he accomplished a great deal that contributed to the global advancement of modern neurosurgery. CONCLUSION: Dr. Turkman's contributions and achievements have inspired many neurosurgeons trained at Ankara and Hacettepe Universities, Neurosurgery Departments in Turkey, and around the world. We honor Dr. Turkman and pay tribute to his memory.

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)0.002
metaresearch head score (Gemma)0.006
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: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.013
Threshold uncertainty score0.028

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0110.006
Scholarly communication0.0030.003
Open science0.0010.002
Research integrity0.0030.012
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 head0.027
GPT teacher head0.288
Teacher spread0.261 · 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

Citations0
Published2023
Admission routes1
Has abstractyes

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