INSTITUTIONALIZATION PATTERNS IN BREAST CANCER IMMUNOHISTOCHEMISTRY
Bibliographic record
Abstract
Purpose: The objective of the present study is to analyze scientometrically some essential patterns of the dynamic science institutionalization on breast cancer immunohistochemistry and to outline the most significant scientists, journals, scientific institutions conference proceedings in this interdisciplinary field. Material/Methods: In December 2019, a retrospective problem-oriented, title-word based search was performed in Web of Science Core Collection (WoS), MEDLINE and BIOSIS Citation Index (BIOSIS) of Web of Knowledge as well as in Scopus for 2003-2018. The following parameters were comparatively assessed: annual dynamics of publications, author’s names, journal titles, scientific institutions, and scientific forums. Results: There were 1187 publications abstracted in WoS, 776 publications abstracted in BIOSIS, 711 publications abstracted in Scopus, and 616 publications abstracted in MEDLINE. There were journal articles in 288 journals abstracted in WoS, in 234 journals abstracted in MEDLINE, in 156 journals abstracted in Scopus, and in 140 journals abstracted in BIOSIS. The most productive authors were David G. Hicks, Rohit Bhargava and Ian O. Ellis. The ‘core’ journals were Modern Pathology, Laboratory Investigation and Cancer Research. The most influential scientific institutions were the University of Texas System (USA) and the University of Texas MD Anderson Cancer Center (USA). The annual meetings of the US and Canadian Academy of Pathology and the Annual San Antonio Breast Cancer symposia were most attractive to the investigators worldwide. Conclusion:Our comprehensive results could be of interest to breast cancer researchers and clinicians from smaller countries, institutional science managers, and journal editors.
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 | 0.006 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.029 | 0.037 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 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".