7TH INTERNATIONAL SYMPOSIUM “SMART LION”. REHABILITATION IN UKRAINE, SEPTEMBER 26, 2023
Bibliographic record
Abstract
The seventh international symposium, SMART LION, was held on September 26, 2023. It was to celebrate the 150th anniversary of the Shevchenko Scientific Society and the 125th anniversary of the Medical Commission. The topics concerned the challenges our country faces today. Organizers of SMART LION 2023 are Professors Valentyna Chopyak, Oksana Zayachkivska, and Vassyl Lonchyna. They managed to gather almost two hundred participants offline and over a thousand guests online. Olena Lazareva, Vira Rokoshevska, Oleh Bilianskyi, Oksana Hdyria, and Oleh Fitkalo delivered their reports on the development of rehabilitation in Ukraine and its main avenues. The emergence of post-traumatic stress disorder (PTSD) is a critical challenge for contemporary Ukraine and has long been a significant obstacle for global medicine. That is the reason why Oleh Berezyuk, Head of the Mental Health Service of the UNBROKEN National Rehabilitation Center, brought up the matter of difficulties in correctly diagnosing and treating PTSD in the context of a multidisciplinary hospital, while Professor Valentyna Chopyak and MD Svitlana Zubchenko focused the attention of attendees on PTSD immune-rehabilitation. During the concluding block of the symposium, which focused on the contemporary interpretation of physical therapy and occupational therapy, as well as interprofessional education, communication, and cooperation, attendees were afforded the chance to listen to highly intriguing reports presented by Renata Roman (Canada), Ellen Godwin (USA), Karl J. Sandin (USA), David Omut (USA), and Oksana Zayachkivska (Ukraine, USA). The scientific event concluded with a round table on the future of physical therapy and rehabilitation in Ukraine, with its outcomes leading to the resolution of the seventh international symposium, SMART LION 2023.
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 | 0.016 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.005 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".