Strategies for optimizing treatment with efalizumab in moderate to severe psoriasis
Bibliographic record
Abstract
With the advent of biological therapies for the treatment of plaque psoriasis, guidance on the usage of these new agents has become necessary. One such agent, efalizumab, a humanized recombinant monoclonal IgG(1) antibody developed to target T-cell-mediated inflammation, provides rapid and sustained efficacy for many psoriasis patients. This article explores the pretreatment, initiation, and treatment phases with efalizumab therapy. In the pretreatment phase, physicians need to assess patients' disease state and educate them about the course of efalizumab treatment. Prior to initiation, physicians need to establish stable disease, ensure an adequate transition or washout of any prior psoriasis therapeutics, and obtain baseline platelet counts. After initiating treatment, both physician and patient must participate in disease monitoring. Patients responding favourably may receive continuous treatment. Those who do not respond to the drug or who experience adverse events should be managed appropriately in order to continue therapy or be transitioned onto another agent. A growing body of clinical evidence, as well as experience from clinical investigators, has provided much insight into the management strategies for patients undergoing treatment with efalizumab.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".