Induction immunosuppression after heart transplantation: monoclonal vs. polyclonal antithymoglobulins. Is there a difference?
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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) | 0.000 | 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 it