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Record W2620664699 · doi:10.1017/cjn.2017.84

C.06 Retraction of scientific publications in neurosurgery

2017· article· en· W2620664699 on OpenAlexaffvenue
Wang Jz, NM Alotaibi, Jerry C. Ku, J. T. Rutka

Bibliographic record

VenueCanadian Journal of Neurological Sciences / Journal Canadien des Sciences Neurologiques · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicAcademic integrity and plagiarism
Canadian institutionsSystems, Applications & Products in Data Processing (Canada)Toronto Public Health
Fundersnot available
KeywordsImpact factorNeurosurgeryMEDLINEMedicineData extractionMisattribution of memoryScientific misconductFamily medicineAlternative medicineSurgeryPolitical sciencePsychiatryPathologyLaw

Abstract

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Background: Despite increasing awareness of scientific fraud, no attempt has been made to assess its prevalence in neurosurgery. The aim of our review was to assess the chronological trend and reasons for the retraction of neurosurgical publications. Methods: We searched the EMBASE and MEDLINE databases using a comprehensive search strategy for retracted articles from January 1995 to December 2016. Archives of retracted articles on www.retractionwatch.com and the independent websites of neurosurgical journals were also searched. Data including the journal name and its impact factor, reason for retraction, country of origin, and citations were extracted. Results: A total of 72 studies were included for data extraction. Journal impact factor ranged from 0.24 to 14.4. Most studies(76%) were retracted within the last 5 years. The most common reason for retraction was because of a duplicated publication found elsewhere(25%), followed closely by plagiarism(21%), or falsifying data(17%). Other reasons included scientific errors/mistakes, author misattribution, and fraudulent peer review. Articles originated from several different countries and some were widely cited. Conclusions: Retractions of neurosurgical publications are increasing globally, mostly due to issues of academic integrity. Implementation of more transparent data sharing and screening as well as additional education for new researchers may help mitigate these issues moving forward.

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
gemmaMetaresearchResearch integrity
Domain: Evaluation · Genre: Empirical
About the Canadian research system: no · About a Canadian topic: no
Systematic reviewhigh
gptMetaresearchBibliometricsScholarly communicationResearch integrity
Domain: Evaluation · Genre: Review
About the Canadian research system: no · About a Canadian topic: no
Systematic reviewhigh
models splitAgreement 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)0.021
metaresearch head score (Gemma)0.024
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Science and technology studies, Scholarly communication, Research integrity
Consensus categoriesScience and technology studies
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.313
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0210.024
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0110.023
Scholarly communication0.0020.003
Open science0.0040.000
Research integrity0.0010.003
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 head0.074
GPT teacher head0.324
Teacher spread0.250 · 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.

MetaresearchResearch integrityBibliometricsScholarly communication

The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.

Study designSystematic review
DomainEvaluation
GenreEmpirical · Review

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
Published2017
Admission routes2
Has abstractyes

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