A bibliometric analysis of the research hotspots in the applications of internet searches in breast cancer patients (Preprint)
Bibliographic record
Abstract
BACKGROUND Women diagnosed with breast cancer need information and support to cope with their illness. The internet has become a powerful way to disseminate health information. The aim of this study is to investigate the research hotspots in the field of the application of the internet in breast cancer patients for information exchange and dissemination. OBJECTIVE Papers published between 1998 and 2016 with major Medical Subject Headings (MeSH) from related citations were downloaded from the PubMed database. METHODS Terms appearing in more than 10 papers were kept as high frequency MeSH terms. Terms that appeared in more than 10 papers were considered high frequency MeSH terms and were retained. A co-occurrence matrix was built with co-occurrence times between each pair of high frequency MeSH terms. These terms were then clustered into four groups. Papers that fell outside of our initial search timeline (published in 2017-2018) were classified into one of the 4 existing clusters of hotspots to predict the emerging trends of the field. RESULTS A total of 459 MeSH terms appeared 1594 times in 313 citations and 26 MeSH terms presented more than 10 times in 809 citations. We clustered these high frequency MeSH terms into 4 groups. Topics of those 18 new papers include online support group related, diagnosis, treatment and prevention, but showed a significant content expansion of each cluster. CONCLUSIONS A total of 459 MeSH terms appeared 1594 times in 313 citations and 26 MeSH terms presented more than 10 times in 809 citations. We clustered these high frequency MeSH terms into 4 groups. Topics of those 18 new papers include online support group related, diagnosis, treatment and prevention, but showed a significant content expansion of each cluster.
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How this classification was reachedexpand
The three-model screen
all 5,600 screened works →All three models called this metaresearch. It is in the settled core of the field.
Bibliometric analysis of research hotspots in a literature on internet searches in breast cancer; the method is bibliometric and the object is a body of research output, matching the borderline bibliometric case.
The bibliometric analysis studies a research literature and its hotspots, making research output the object rather than merely using bibliometrics.
Bibliometric mapping of research hotspots on internet-search applications in breast cancer; studies a research literature, borderline T2.
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 | 0.004 | 0.041 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.090 | 0.123 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".