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Record W2379664676

Bibliometrical Analysis of Cerebral Palsy Research in Recent 5 Years

2012· article· en· W2379664676 on OpenAlexaboutno aff
Jiaqing Wang

Bibliographic record

VenueTestowy IndexCopernicus · 2012
Typearticle
Languageen
FieldMedicine
TopicNeonatal and fetal brain pathology
Canadian institutionsnot available
Fundersnot available
KeywordsCerebral palsyBibliometricsChinaNeurosurgeryRehabilitationWeb of scienceMedicinePediatricsPolitical sciencePhysical therapyLibrary scienceSurgeryMeta-analysisComputer sciencePathology
DOInot available

Abstract

fetched live from OpenAlex

Objective To understand current research development and research focus of cerebral palsy and predict the trend of future development trend in this area. Methods Web of Science database was searched and gained 5 230 articles were gained concerning cerebral palsy from Jan.2008 to Aug.2012.Country,language,authors,major journals,and frequency of subject headings based on bibliometrics methodologies were analyzed. Results In recent 5 years on cerebral palsy related researches involving a total of 50 countries or regions.The American authors published the most papers,1 658 papers of the total amount of literature,31.70%;followed by Canada,424 papers,8.11% of the total amount of literature,China ranked twelfth,a total of 131 papers the total amount of literature,2.51%.Articles were the most type of the cerebral palsy research papers and English was still the dominating language,and that major journals covers almost all international prestigious journals of neurosurgery,pediatrics,and rehabilitation. Conclusions Cerebral palsy is currently the research focus in the field of pediatric neurology,especially research on the rehabilitation,gross motor function,therapy and so on.Related researches in China and the United States,Britain and other countries there is a gap,Chinese pediatric worker should make more efforts on the study of cerebral palsy.

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 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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesBibliometrics, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.056
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0100.030
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.147
GPT teacher head0.410
Teacher spread0.263 · 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 teacher head, not a consensus.

Study designObservational
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
Published2012
Admission routes1
Has abstractyes

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