Characterizing the effect of the COVID-19 pandemic on the orthopaedic surgery literature
Bibliographic record
Abstract
While the novel Coronavirus 2019 disease’s (COVID-19) impact on the practice of orthopaedics has been readily apparent, the effects of COVID-19 on the orthopaedic literature has not been studied. The objective of this paper is to analyze the COVID-19 pandemic’s impact on peer-reviewed articles published in the orthopaedic surgery literature. Using the Journal Citation Reports, twenty orthopaedic surgery journals with the highest impact factor in 2019 were selected and articles within those journals were sorted by mention of COVID-19. The Altmetric Attention Score (AAS) and citation count were collected and compared for COVID-19 versus non-COVID-19 related articles using the Mann-Whitney U test. Furthermore, within COVID-19 related articles, AAS and citation count were compared using Kruskal-Wallis test between sub specialty of orthopaedics, type of article, study type, and quarter of publication. The average AAS of COVID-19 articles was significantly higher than non-COVID articles (15 vs. 6, p=0.019). Within COVID-19 articles, those pertaining to spine and trauma had a significantly lower AAS than those pertaining to orthopaedics as a whole (20 & 6 vs 51, p<0.001). The average number of citations accrued by COVID-19 articles was significantly higher than non-COVID-19 articles (8 vs. 1, p<0.001). Original COVID-19 articles received significantly more citations than editorial articles (10 vs. 5, p<0.001), as well as those published in the second quarter of 2020 compared to those published later (p<0.001). Orthopaedic articles related to COVID-19 demonstrated a greater influence, dissemination, and impact than articles not related to COVID-19 as demonstrated by AAS and citations accrued.
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 | 0.014 | 0.097 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.039 | 0.034 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 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".