Predictors of Citation Rate for Original Research Studies in the <i>Canadian Association of Radiologists Journal</i>
Bibliographic record
Abstract
OBJECTIVE: This study is aimed to identify predictors of citation rate of original research published in the Canadian Association of Radiologists Journal (CARJ). METHODS: A search of MEDLINE was conducted from January 1, 2000-June 30, 2013 to identify all studies published in the CARJ. Original research studies were included. Reviews, pictorial essays, guidelines, case studies, case series, and original studies with a sample size <10 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 = .017) and number of references (r = 0.164, P = .028) demonstrated statistically significant weak 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 5 or more years after the publication. Sample size and number of references were identified as significant, but weak predictors of citation rate in CARJ.
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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 | 0.046 | 0.357 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.039 | 0.047 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.002 |
| 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".