Bibliographic record
Abstract
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 machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.165 | 0.206 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".