Determining the impact factors of secondary journals: A retrospective cohort study
Bibliographic record
Abstract
Abstract Secondary journals such as Evidence‐Based Medicine, ACP Journal Club, and Evidence‐Based Nursing summarize, from over 150 clinical journals, articles that pass criteria for scientific merit, clinical relevance, and interest to practicing clinicians. We performed a retrospective cohort study to validate the selection process used to produce the secondary journals by calculating the 2007 impact factors for these journals using articles that were abstracted and originally published in 2005–2006. The ‘impact factors’ for the secondary journals were calculated using 2007 citations to included articles. These were compared to the published impact factors and mean citations of the source journals. 2005/2006 articles in the secondary journals were originally published in 82 journals with ISI impact factors (median 4.1, range 0.85–52.9). The calculated impact factors for the secondary journals were 39.5 for ACP Journal Club, 30.2 for Evidence‐Based Medicine, and 9.3 for Evidence‐Based Nursing. Limitations include articles coming from only 150 journal titles and the inclusion of these articles may in fact stimulate citations. We conclude that evidence‐based secondary journals include articles at the time of publication that go on to garner more citations on average than other articles in the source publications.
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
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | MetaresearchBibliometrics Domain: Evaluation · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | BibliometricsMetaresearch Domain: Evaluation · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
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 | 0.009 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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, unvalidatedLabeled directly by 2 models reading the full record.
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".