Accelerated induction regimens of TNF-alpha inhibitors in patients with inflammatory bowel disease: a scoping review protocol
Bibliographic record
Abstract
INTRODUCTION: Tumour necrosis factor (TNF)-alpha inhibitors are commonly used to treat inflammatory bowel disease (IBD). In patients with IBD who are unresponsive to their first induction dose, the implementation of an 'accelerated' induction dose schedule (doses more frequent than recommended in product monographs) is becoming increasingly common. It is unclear whether this practice results in favourable patient outcomes, such as avoidance of surgery and disease remission. As such, there is a need to identify and map the current evidence base on accelerated induction schedules of these medications in the treatment of IBD. METHODS AND ANALYSIS: A scoping review will be employed to systematically identify and characterise the nature of scientific literature on accelerated induction regimens of TNF-alpha inhibitors. MEDLINE, Embase, International Pharmaceutical Abstracts and grey literature will be searched to identify relevant studies. The titles/abstracts of all records and full text of potentially relevant articles will be independently screened for inclusion by two reviewers. Data will be abstracted from included studies by one reviewer and verified for accuracy by another. The findings will be synthesised descriptively. ETHICS AND DISSEMINATION: We intend to report the findings of this scoping review in a peer-reviewed journal and a scientific conference. TRIAL REGISTRATION: This research was registered prospectively with the Open Science Framework (https://osf.io/z7n2d/).
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 | 0.050 | 0.038 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.011 | 0.010 |
| Bibliometrics | 0.012 | 0.009 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.007 | 0.004 |
| Insufficient payload (model declined to judge) | 0.056 | 0.009 |
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".