Update on therapy — thalidomide in the treatment of lupus
Bibliographic record
Abstract
Thalidomide has been shown to be an effective treatment for cutaneous forms of lupus erythematous refractory to other therapies. Thalidomide has very serious side effects, including teratogenicity and neuropathy, which limit its clinical use in lupus to such severe refractory cases. Efficacy has been confirmed in several studies, although recurrence after discontinuation of treatment is frequent. More recent experience suggests that lower doses than originally used may be effective, which may result in a reduction in side effects. Much effort has been expended in studying the mechanisms of action of thalidomide, although as yet it is unclear which of the mechanisms identified to date contribute to its efficacy in treating cutaneous forms of lupus erythematosus. Identification of patients suitable for thalidomide therapy requires a rigorous selection process. Potential side effects should be clearly explained, particularly teratogenicity as many patients are young women. Written consent and a negative pregnancy test must be obtained prior to commencement of therapy. Reliable contraceptive measures should be strictly observed by patients taking thalidomide. Close clinical and neurophysiological supervision using nerve conduction studies should be undertaken.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.005 |
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".