Author Self-Citation in the General Medicine Literature
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND: Author self-citation contributes to the overall citation count of an article and the impact factor of the journal in which it appears. Little is known, however, about the extent of self-citation in the general clinical medicine literature. The objective of this study was to determine the extent and temporal pattern of author self-citation and the article characteristics associated with author self-citation. METHODOLOGY/PRINCIPAL FINDINGS: We performed a retrospective cohort study of articles published in three high impact general medical journals (JAMA, Lancet, and New England Journal of Medicine) between October 1, 1999 and March 31, 2000. We retrieved the number and percentage of author self-citations received by the article since publication, as of June 2008, from the Scopus citation database. Several article characteristics were extracted by two blinded, independent reviewers for each article in the cohort and analyzed in multivariable linear regression analyses. Since publication, author self-citations accounted for 6.5% (95% confidence interval 6.3-6.7%) of all citations received by the 328 articles in our sample. Self-citation peaked in 2002, declining annually thereafter. Studies with more authors, in cardiovascular medicine or infectious disease, and with smaller sample size were associated with more author self-citations and higher percentage of author self-citation (all p≤0.01). CONCLUSIONS/SIGNIFICANCE: Approximately 1 in 15 citations of articles in high-profile general medicine journals are author self-citations. Self-citation peaks within about 2 years of publication and disproportionately affects impact factor. Studies most vulnerable to this effect are those with more authors, small sample size, and in cardiovascular medicine or infectious disease.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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 it