Abstracts of the 7 <sup>th</sup> Asian Pain Symposium
Bibliographic record
Abstract
Summary The Asian Pain Symposium (APS) is a main pain research meeting in Asia. Since established in 2000 in Kyoto, five other APSs have been held in different Asian regions including Seoul of Korea in 2004, Fukuoka of Japan in 2008, Shanghai of China in 2011, Okazaki of Japan in 2013, and Suzhou of China in 2015. The 7 th Asian Pain Symposium (APS 2017) was held in Taipei of Taiwan during October 26th to October 29th, 2017. The APS 2017 was sponsored by The Ministry of Science and Technology of Taiwan and Institute of Biomedical Science and Neuroscience Program of Academia Sinica and Taiwan Pain Society. The president of the APS 2017 was Dr. Bai Chuang Shyu, Institute of Biomedical Sciences, Academia Sinica, Taiwan. Local organizing committee also include Dr. Jen-Chuen Hsieh, Institute of Brain Science, National Yang-Ming University and Veteran General Hospital, Taiwan, Dr. Wei-Zen Sun, Department of Anesthesiology, National Taiwan University Hospital, Taiwan, and Dr. Chih-Cheng Chen, Institute of Biomedical Sciences, Academia Sinica, Taiwan. Main topics of the APS 2017 included the latest progress of pain research and novel strategies of pain treatments. Symposium attendees presented their interesting and exciting research findings in the areas of 1) basic sensory and nociceptive functions, 2) ion channels and their functions in somatosensory physiology and pain, 3) brain functions and regulations in pain, 4) spinal cord mechanisms of nociception and pain, 5) analgesia and pain regulations, 6) chronic pain mechanisms and treatment, and 7) brain circuits underlying the physiological and pathological pain. There were a total of 29 oral presentations and 23 poster presentations at the 7 th APS. A council meeting was held during the 7 th APS, and at this council meeting Dr. Seog Bae OH (Seoul National University) was elected as the president of 8 th Asian Pain Symposium to organize the next symposium in Seoul, Korea in 2019. In order to keep a permanent record and to help promote pain research in Asia, we have collected abstracts of oral presentations and posted them below in the order when the presentations were given at the 7 th Asian Pain Symposium.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.213 | 0.117 |
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".