Bibliographic record
Abstract
Tests measuring serum adalimumab levels are not widely available. We aim to evaluate whether serum adalimumab levels correlate with disease severity in patients with Crohn disease. Additionally, as the test is expensive, we aim to see if clinical and biochemical markers can be used as a surrogate for adalimumab levels. A retrospective chart review was performed on Crohn disease patients that had a measured adalimumab level. One hundred forty-nine patients were identified between January 2015 and August 2017 at London Health Sciences Center. Disease severity was determined using the Harvey-Bradshaw Index (HBI). Out of 149 patients, 100 were in remission. Mean trough adalimumab level was 8.7 and 6.5 for remission and active disease groups respectively. Patients in remission had a mean weight of 78.4kg compared to 101.4kg in patients with active disease. Serum adalimumab levels correlated with HBI, weight and log CRP. The respective pearson correlation coefficients were r= -0.19 (p=0.018), r= -0.24 (p=0.005), r= -0.40 (p=0.0004). There were no statistically significant correlations between trough adalimumab level and albumin. Nor were there any significant correlations between HBI and weight, albumin or log CRP. Similar results were seen when stratified based on weekly (n=32) or biweekly dosing (n=117). Higher trough adalimumab levels correlated with lower disease activity, lower weight and lower CRP in patients with Crohn disease. Patients with higher disease burden and increased weight may benefit from empiric weekly dosing of adalimumab. These results also lend support for increasing adalimumab dosing in non-responders. Ultimately, larger studies with prospective data may yield more helpful information. None
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".