Research hotspots and trends in internal fixation of femoral neck fractures from 2010 to 2022: A 12-year bibliometric analysis
Bibliographic record
Abstract
BACKGROUND: This study endeavors to scrutinize the hotspots and trends in the literature concerning the internal fixation of femoral neck fractures (INFNF) through a comprehensive bibliometric analysis. Notably, this analytical process encompasses both qualitative and quantitative components. METHODS: The present study has utilized the Science Citation Index-Expanded from the Web of Science Core Collection to extract datasets ranging from January 1, 2010, to August 31, 2022. Quantitative analysis was carried out using sophisticated analytical tools such as the Bibliographic Item Co-Occurrence Matrix Builder, the Online Analysis Platform of Literature Metrology, and CiteSpace software. Further, the major Medical Subject Headings terms and their subheading counterparts associated with INFNF were extracted from the PubMed2XL website using the corresponding PMIDs. These Medical Subject Headings terms were employed in conducting a co-word clustering analysis. Ultimately, the Graphical CLUstering TOolkit program was utilized to execute a co-word biclustering analysis to discern the prevailing hotspots in this domain. RESULTS: Between January 1, 2010, and August 31, 2022, a total of 463 publications were issued on INFNF. The INJURY-INTERNAL JOURNAL OF THE CARE OF THE INJURED stood out as the most extensively perused journal in this area. Notably, China emerged as the foremost contributor to publishing articles within the last 12 years, followed by the United States and Canada. McMaster University was identified as the leading institution in INFNF research, while Bhandari M emerged as the most prolific author in this field. Moreover, the study identified five notable research hotspots within the domain of INFNF. CONCLUSIONS: This study has identified five critical areas of research in the field of INFNF. It suggests that the primary focus of future research is likely to center on advancing internal fixation methods and robot-assisted instrumentation for femoral neck fractures. As such, this study provides valuable insights into future research directions and ideas for those working in this field.
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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 | 0.006 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.111 | 0.138 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".