Lenalidomide Desensitization in Systemic Light-Chain Amyloidosis With Multi-Organ Involvement
Bibliographic record
Abstract
Limited therapeutic options are available to amyloid patients treated with many lines of therapy. Although combination therapy using lenalidomide and dexamethasone is an effective sequential regimen for systemic amyloidosis (AL), dexamethasone is often poorly tolerated in patients with cardiac involvement. Lenalidomide as single agent has modest activity, but when used in combination with dexamethasone, careful titration is needed. Dermatological adverse reactions can be problematic to patients on lenalidomide-based therapy. Lowering lenalidomide doses have not been able to consistently prevent recurrent skin toxicity. We report a patient who was neither eligible for stem cell transplant nor able to tolerate previous lines of therapy. Therapeutic dilemma arose from lenalidomide-related moderately severe skin toxicity. We enrolled the patient in the lenalidomide rapid desensitization program (RDP) with success in the presence of poor cardiac reserve and renal impairment. No recurrence of skin rash was observed during the course of therapy. To the best of our knowledge, this was the first AL patients who received and tolerated RDP well, despite multi-organ impairments. The target dose may be achieved based on individual patient's ability to tolerate RDP. Incremental dose increase can be applied in future dates without risk of rash recurrence.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".