Global trends of researches on pycho-oncology during 1999-2019: A 21-year bibliometric study based on VOSviewer
Bibliographic record
Abstract
Abstract BackgroundThe studies on psycho-oncology are growing rapidly, but there were no bibliometric studies regarding psycho-oncology. This study was to explore a bibliometric analysis of psycho-oncology in the past 21 years at a global level.MethodsThe literature was searched in Web of Science (WOS) by using subject terms. VOSviewer software was used for bibliometric analysis of the retrieval results.ResultsThe literature search yielded 1921 papers. After screening process, 968 papers were included, which came from 55 countries/regions, 1,452 organizations and 4,152 authors. The top three countries/regions, organizations and authors ranked by the number of published papers were the United States of America (USA) (286), Germany (143) and Australia (130); the Memorial Sloan-Kettering Cancer Centre (MSKCC) (New York, USA) (34), Newcastle University (Newcastle, Australia) (29) and McGill University (Montreal, Canada) (28); Luigi Grassi (University of Ferrara in Italy) (26), Tatsuo Akechi (Nagoya City University Hospital in Japan) (20) and Anja Mehnert (University of Leipzig in Germany) (18), respectively. Moreover, the 968 papers contained 1,768 author keywords, involved in 300 journals and cited 28,311 references. The top three co-occurrence author keywords, most-involved journals and most-cited references were “Quality-of-life”, “Depression” and “Breast cancer”; Psycho-Oncology, Supportive Care in Cancer and Journal of Psychosocial Oncology; “Zigmond AS, 1983”, “Zabora J, 2001” and “Mitchell AJ, 2011”, respectively.ConclusionsThere was a growing trend in published papers related to psycho-oncology, with the organizations and authors from developed countries leading the field. “Quality-of-life”, “Depression” and “Breast cancer” reflected the most hotspots, and the latest progress can be tracked in Psycho-Oncology.
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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 | 0.010 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.157 | 0.211 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".