The Scholarly Influence of Orthopaedic Research According to Conventional and Alternative Metrics
Bibliographic record
Abstract
BACKGROUND: Researchers are experiencing an innovative shift toward online distribution of their work, and metrics related to online scholarly influence are gaining importance. Our objectives were to determine which types of online activity are most prevalent in orthopaedics, to identify associated factors, and to explore a complementary approach to measuring overall scholarly influence using online activity and conventional citations. METHODS: We performed a systematic review of randomized controlled trials of surgical or nonsurgical interventions in participants with, or at specific risk for, injuries and diseases of the musculoskeletal system. We collected data on online activity in social media, mainstream media, blogs, forums, and other sources from a commercial provider of alternative metric data for medical journals. We tested associations with use of negative binomial regression. RESULTS: We identified 1,697 trials, published between 2011 and 2014, that had a total of 12,995 conventional citations and 15,068 online mentions. The median number of online mentions of each trial was 2 (interquartile range, 0 to 5). Twitter (82%) and Facebook (13%) mentions were the most prevalent types of online activity. Counts of online mentions correlated with conventional citations (r = 0.11, p < 0.01) but accumulated more rapidly. Higher total counts of online mentions were consistently associated with longer time since publication, higher journal impact factor, higher author h-index values, and less risk of bias (p < 0.01 for each). We found the best model fit for a complementary approach by weighting citations and online mentions equally. CONCLUSIONS: Online activity in orthopaedics is dominated by activity on Twitter and Facebook and is associated with increasing time since publication, journal impact factor, and author h-index values, and less risk of bias. Institutions, publishers, funding agencies, and clinicians may consider a complementary approach to measuring scholarly influence that weights online mentions and conventional citations equally.
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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 | 0.230 | 0.604 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.013 |
| Bibliometrics | 0.038 | 0.045 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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 itClassification
machine, unvalidatedMachine predicted; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".