Dose Escalation of Adalimumab in Patients with Hidradenitis Suppurativa: A Retrospective Case Series
Bibliographic record
Abstract
Background: Adalimumab is a central treatment for moderate-to-severe hidradenitis suppurativa (HS). However, half of patients treated with adalimumab for HS do not achieve a clinically significant response. Data on non-immunological factors correlating clinical response and adalimumab blood concentration are scarce. Objectives: To determine whether increasing adalimumab dose to 80 mg weekly can improve the clinical outcome of patients unresponsive to adalimumab 40 mg weekly. To identify parameters influencing disease activity and those modifying serum adalimumab concentration. Methods: This is a retrospective case series from a tertiary dermatology clinic, comprising 40 patients with moderate-to-severe HS with suboptimal response to the FDA-approved dose of adalimumab. All patients had a measurement of blood adalimumab concentration. Depending on their dosage, some patients had their adalimumab dose increased to 80 mg weekly while others stayed at 40 mg dose weekly. Chi-squared and ANOVA tests were used for data analysis. Results: 43.8% of patients who increased to a weekly dose of 80 mg clinically improved at their follow-up, compared with 33.3% of those who stayed at a weekly dose of 40 mg. The dose increase led to a statistically significant increase in serum concentration of adalimumab ( P < .001). Higher serum adalimumab concentration is associated with lower disease activity ( P = .043). Normal-weight patients had significantly higher concentrations than overweight patients ( P = .011). Conclusions: An increase in the weekly dose of adalimumab led to a nonstatistically significant improvement in clinical response but led to a statistically significant increase in serum concentration.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".