Bibliographic record
Abstract
BACKGROUND: This study aimed to show the world research productivity in the field of back pain and to help researchers follow the scientific development and promote the cooperation in this field. METHODS: Web of Science (WoS) database was searched from 1995 to 2016 without other restrictions. The keywords were as follows: "lumbar NEAR pain," "back pain," "dorsalgia," "backache," "lumbago," "back NEAR disorder," and "discitis." The following information of retrieved articles was analyzed: countries/territories, journals, publication year, authors, citation reports, and institutions. Publication activity was further adjusted for countries by gross domestic product (GDP) and population size. RESULTS: A total of 50,970 articles were retrieved in WoS database from 1995 to 2016. The United States published the biggest number of articles (16,818, 33.00%), followed by England (4,582, 8.99%), Germany (3,871, 7.60%), Canada (3,613, 7.09%), and Australia (3,063, 6.01%). Sweden ranked the first after adjusted for publication, and Netherlands ranked the first after adjusted for GDP. Besides, there was positive correlation between total number of publications and GDP for each country (P < .05). Harvard University was the most productive institution (917, 1.80%), Maher CG was the most productive author (229, 0.45%) and Spine was the most popular journal (3605, 7.07%) in the field of back pain research. Moreover, the article titled "Clinical importance of changes in chronic pain intensity measured on an 11-point numerical pain rating scale" in Pain had the highest citations (1749). CONCLUSION: There was a significant increase in annual publications concerning back pain research worldwide. The total number of publications was positively associated with GDP in main productive countries. The United States was the most productive country, Harvard University was the most productive institution, Maher CG was the most productive author and Spine was the most popular journal in the field of back pain.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.021 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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; a candidate call from one teacher head, 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".