Induction immunosuppression after heart transplantation: monoclonal vs. polyclonal antithymoglobulins. Is there a difference?
Bibliographic record
Abstract
Induction immunosuppression after heart transplantation is believed to reduce the risk of acute graft rejection. While monoclonal and polyclonal antithymoglobulins are considered the optimal induction agents, controversy remains regarding their relative superiority. This article presents a systematic review of the literature and a meta-analysis in order to assess the relative benefits and side-effects of monoclonal vs. polyclonal antithymoglobulins as induction immunosuppression agents. Pooled analysis demonstrated a small but statistically insignificant difference in the average time to first rejection between the groups (6.7+/-15.5 days, P=0.39). No statistically significant differences in the proportion of patients who developed rejection or infection episodes at 6 months were observed (Relative Risk 0.97, P=0.82 and Relative Risk 0.85, P=0.14, respectively). In addition, no statistically significant difference in survival was found between the groups at 6 months (Relative Risk 0.98, P=0.58). A greater number of drug related side-effects was observed, however, in the monoclonal group, including episodes of acute pulmonary edema and hypotension. In conclusion, this review revealed no statistically significant differences in rejection, infection, or survival rates between the monoclonal and polyclonal groups. The increased rate of side-effects with monoclonal antibodies might suggest a superiority of polyclonal over monoclonal antibodies.
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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 | 0.008 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".