Predictors of Citation Rate in Original Research CARJ Publications
Bibliographic record
Abstract
Objective: This study aimed to identify predictors of citation rate of original research studies published in the Canadian Association of Radiologists Journal (CARJ). Methods: A search of MEDLINE was conducted from January 1, 2000 to June 30, 2013 to identify all studies published in the CARJ. Original research studies were included. Reviews, pictorial essays, guidelines, and original studies with a sample size <10 (including case studies and case series) were excluded. Variables assessed for association with citation rate included number of authors, study design, sample size, multi-institutional study, multi-national study, study type, presence of statistically significant result, presence of funding, and number of references. Statistical analysis was completed using linear regression and Pearson correlation coefficients (r).Results: A total of 714 studies were published in CARJ, of which 181 were original research publications that were cited a total of 1517 times. Twelve original research studies were uncited, while the most-cited one was cited 58 times. Sample size (r=0.177, p=0.017) and number of references (r=0.164, p=0.028) demonstrated statistically significant positive correlations with citation rate. Number of authors, study design, setting, statistically significant results, and funding were not associated with citation rate. Conclusion: Only a very small number of original research studies published at the CARJ remained uncited five or more years after the publication. Sample size and number of references were the only identified predictors of citation rate in CARJ.
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 | 0.053 | 0.376 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.042 | 0.049 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| 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".