A Bibliometric Analysis of Publications on Oxycodone from 1998 to 2017
Bibliographic record
Abstract
BACKGROUND: Oxycodone is a widely used opioid analgesic, which is involved in cancer pain and non-cancer pain. This study is intended to understand the publication characteristics of oxycodone research field and assess the quality of pertinent articles from 1998 to 2017. METHODS: Oxycodone-related publications from 1998 to 2017 were retrieved from the Web of Science (WOS) and PubMed database. These papers were coded across several categories, such as total number, journals, countries, institutions, authors and citations reports. And the analysis of co-occurrence keywords was handled by VOSviewer software. RESULTS: According to search strategies, a total of 2659 articles on oxycodone were published in world from 1998 to 2017 in WOS. Among the top 10 most productive organizations, six of them were American institutes, two of them were pharmaceutical enterprises and the other three were Finnish, Australian and Canadian institutes, which is similar with the distribution by country/region. Drewes AM from Denmark published most articles and PAIN MEDICINE is the most productive journal in oxycodone area. Meanwhile, clinical studies occupy a dominant position during the past 20 years. The 10 most cited papers were listed. Among these articles, 8 of them are reviews and 2 of those are meta-analysis. And the last decade (2008-2017) displayed that the newest keywords focus on "double-blind", "randomized controlled trial" and "neuropathic pain". CONCLUSIONS: The findings provided a comprehensive overview of oxycodone research. In view of the adverse effects of oxycodone, high-quality oxycodone studies both in basic studies and clinical trials need to be completed.
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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 | 0.006 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.155 | 0.190 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".