Subspecialty Impact Factors: The Contribution of Pediatric Anesthesia and Pain Articles
Bibliographic record
Abstract
BACKGROUND: The Science Citation Index "Journal Impact Factor" (JIF) is widely used to assess journal quality and prestige. The JIFs for the specialty anesthesia are reported annually, however, the impact factors (IFs) for subspecialties in those journals have not been reported. Therefore, we compared the IFs of pediatric anesthesia (Ped IFs) and pain (Pain IFs) articles from four anesthesia journals for two epochs. METHODS: An article-by-article manual search for "source" pediatric anesthesia and pain articles published in 1998, 1999, 2003, and 2004 in Anesthesiology, Anesthesia & Analgesia, British Journal of Anaesthesia, and Canadian Journal of Anesthesia was performed. The citations for each of these articles in each of the years were surveyed in the ISI Web of Science database. Ped IFs and Pain IFs for the 2000 and 2005 epochs were calculated and compared with the JIF from which they were derived and to those of the journal, Pediatric Anesthesia. RESULTS: Ped IFs for the four journals in 2005 exceeded those in 2000, whereas the Pain IFs were unchanged. For both the Ped IFs and the Pain IFs, there was a significant effect of the journal. The Pain IFs were 70% greater than the Ped IFs. CONCLUSIONS: Ped IFs were consistently less than the JIFs in which they were published and the Pain IFs, except for the British Journal of Anaesthesia 2005 in the latter case. The numbers of citations of pediatric anesthesia articles were greater in journals with greater IFs. The implications of subspecialty IFs warrant further consideration.
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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 | 0.009 | 0.128 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.054 | 0.067 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".