Splenectomy for immune thrombocytopenia: down but not out
Bibliographic record
Abstract
Splenectomy is an effective therapy for steroid-refractory or dependent immune thrombocytopenia (ITP). With the advent of medical alternatives such as rituximab and thrombopoietin receptor antagonists, the use of splenectomy has declined and is generally reserved for patients that fail multiple medical therapies. Splenectomy removes the primary site of platelet clearance and autoantibody production and offers the highest rate of durable response (50% to 70%) compared with other ITP therapies. However, there are no reliable predictors of splenectomy response, and long-term risks of infection and cardiovascular complications must be considered. Because the long-term efficacy of different second-line medical therapies for ITP have not been directly compared, treatment decisions must be made without supportive evidence. Splenectomy continues to be a reasonable treatment option for many patients, including those with an active lifestyle who desire freedom from medication and monitoring, and patients with fulminant ITP that does not respond well to medical therapy. We try to avoid splenectomy within the first 12 months after ITP diagnosis for most patients to allow for spontaneous or therapy-induced remissions, particularly in older patients who have increased surgical morbidity and lower rates of response, and in young children. Treatment decisions must be individualized based on patients' comorbidities, lifestyles, and preferences. Future research should focus on comparing long-term outcomes of patients treated with different second-line therapies and on developing personalized medicine approaches to identify subsets of patients most likely to respond to splenectomy or other therapeutic approaches.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".