Best Practice Guidance for Adult Infusion Centres during the COVID-19 Pandemic: Report from the COVID-19 International Organization for the Study of IBD [IOIBD] Task Force
Bibliographic record
Abstract
Infusion centres are a central part in the management of patients with inflammatory bowel disease [IBD] and could be a source of transmission of SARS-COV-2. Here we aimed to develop global guidance for best practices of infusion centres for IBD patients and to determine the impact of the COVID-19 pandemic on these centres. Under the auspices of the International Organization for the Study of Inflammatory Bowel Disease [IOIBD], a task force [TF] was formed, an online survey was developed to query infusion centre protocols during COVID-19, and recommendations were made, based on TF experience and opinion. Recommendations focus mainly on patients screening, infusion centres re-organization, personnel protection, and protocol modifications such as shortening infusion duration or replacing it with subcutaneous alternatives. Implementing these recommendations will hopefully reduce exposure of both IBD patients and care givers to SARS-COV-2 and improve the function and safety of infusion centres during the COVID-19 pandemic as well as potential future threats.
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 | 0.054 | 0.114 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.009 | 0.008 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".