The 100 most cited papers on bone metastasis: A bibliometric analysis
Bibliographic record
Abstract
Background: Over the past few decades, a vast number of articles focused on bone metastasis have been published. Bibliometric analysis is helpful to determine the qualities and characteristics and to reveal the influential articles in this field. Methods: All the databases in Web of Science were utilized to identify articles published from 1961 to 2020. The top 100 most cited articles on bone metastases were involved for degree centrality analysis and analyses on publication time and citations, journals, authors, geographical distribution, research institutions, and research keywords. Results: The selected articles were published mainly from 1986 to 2015. The 100 most cited articles were selected from a total of 67,451 citations out of 90,502 publications with a density of 50.239 citations/year. Citations per article ranged from 357 to 2167. The leading country was USA, followed by Canada and United Kingdom. The most frequently studied themes were clinical management of bone metastasis from different malignancy origins. A co-authorship analysis revealed an intense collaborative activity between countries and institutions. Conclusions: This study identified the top 100 most cited articles on bone metastasis. Publication time, area, and theme distribution were thoroughly analyzed. The present study highlighted some of the most influential contributions to the field. Clinical and academic communities have shown a sustained interest in the management of bone metastasis.
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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 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.120 | 0.114 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| 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".