Bibliographic record
Abstract
The conduct of collaborative research faces numerous obstacles, including technical and political barriers, absence of accepted leaders, and budget constraints; however, collaboration is essential to produce meaningful research in a relatively rare disease such as pediatric inflammatory bowel disease (IBD). Biomedical research should be a synergized effort of collaboration between clinicians, scientists, and cutting-edge technology, in which each taps into a unique aspect of the joint project. Approximately 175 years passed from the invention of the steam engine to the light bulb, but it took only 5 years from discovering the nucleotide-binding oligomerization domain-containing protein 2 to cracking the entire human genome. The astounding development of technology with rapid globalization makes networking, as described by Sherman et al (1), the state-of-the-art tool in modern IBD research. In addition to multiple networks, established ad hoc for specific studies, there are several successful ongoing multicenter pediatric IBD partnerships, such as the prolific Pediatric IBD Collaborative Research Group and the ImproveCareNow registry in North America. Pediatric gastroenterologists from Europe collaborate under the umbrella of the Porto Pediatric IBD Working Group of ESPGHAN with annual meetings and rotating leadership. Each network operates under different rules and the described workshop is a promising attempt in seeking the appropriate funded structure for the diverse Canadian environment. Adequate funding is not a sine qua non for successful collaboration, but given that Canada is the home of world-leading IBD researchers and with its superbly organized medical and research infrastructure, undoubtedly invaluable pediatric IBD knowledge will originate from this network.
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 | 0.022 | 0.098 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.003 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.007 | 0.006 |
| Research integrity | 0.038 | 0.038 |
| Insufficient payload (model declined to judge) | 0.120 | 0.057 |
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".