Bibliometric analysis of global research on the rehabilitation of spinal cord injury in the past two decades
Bibliographic record
Abstract
Purpose: We aimed to build a model to qualitatively and quantitatively evaluate publications of research of spinal cord injury rehabilitation from 1997 to 2016. Methods: Data were obtained from the Web of Science Core Collection on October 6, 2017. We conducted a qualitative and quantitative analysis of publication outputs, journals, authors, institutions, countries, cited references, keywords, and terms by bibliometric methods and bibliometric software packages. Results: We identified 5,607 publications on rehabilitation of spinal cord injury from 1997 to 2016, and found that the annual publication rate increased with time. The Archives of Physical Medicine and Rehabilitation published the largest number of literature, the most active country was USA, the most active institution was University of Washington, and Post MWM was the leading author. Keyword analysis indicated that life satisfaction, muscle strength, wheelchair training, walking, gait, and others were the hot spots of these research studies, whereas classification, exoskeleton, plasticity, and old adult were research frontiers. Conclusion: This bibliometric study revealed that research on rehabilitation of spinal cord injury is a well-developed and promising research field. Global scientific research cooperation is close. However, higher quality research is needed. Our findings provide valuable information for researchers to identify better perspectives and develop the future research direction. Keywords: scientific collaboration network, co-citation network, keywords co-occurrence network, journal analysis, cited reference analysis
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 | 0.020 | 0.124 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.179 | 0.282 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".