SU‐E‐T‐237: Leading 25 in 25: A Bibliometric Analysis of Classics Articles in IMRT
Bibliographic record
Abstract
PURPOSE: Scientific publication in IMRT has experienced significant development since its introduction. Bibliometric analyses allow an understanding of how this publication trend is organized at an aggregated level. Citation analysis is one of the most widely used bibliometric tools of scientometrics. Analysis of classics, defined as an articles with 100 or more citations, is common in the biomedical sciences as it reflects an article's influence and the recognition it receives in its professional and scientific community. Our objective was to identify the 25 most frequently cited classic articles in IMRT in the past 25 years. METHODS: The 25 most-cited IMRT articles were identified by searching ISI's Web of Knowledge and Pubmed databases for all related publications from 1986 through 2011. Articles were evaluated for several characteristics such as author(s), institution, country of origin, publication year, type, and number of citations. An unadjusted categorical analysis was performed to compare all articles published in the search period. RESULTS: Our search yielded a cumulative total of 37,197 entries for the publication period. Original research and clinical trial articles accounted for 25,827 and 312, respectively, for a total of 25,719 entries. The 25 most-cited articles were identified from the latter sum and selected out of 123 classics. The number of citations per article ranged from 203 to 504. IMRT classics appeared in one general and five core journals. CONCLUSIONS: This study analyzed publication patterns focused on IMRT, its most influential classic articles, journals that published them, their authors and other salient characteristics. Such information is of importance to researchers and those who wish to study the history and development of the field.
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 | 0.004 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.065 | 0.097 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".