The Impact of Different Induction Immunosuppressive Therapy on Long-Term Kidney Transplant Function When Measured by Iothalamate Clearance
Bibliographic record
Abstract
BACKGROUND: Improvement in short-term outcomes after kidney transplant has been achieved by using different induction and maintenance therapeutic approaches. Long-term outcomes have not matched the expectations of the transplant stakeholders. Our study aimed to address the early impact of induction agents on long-term outcome of kidney transplant when measured by iothalamate clearance. METHODS: All adult kidney transplant recipients between January of 2012 and December of 2016 were reviewed. Six hundred forty-nine patients were divided into three groups based on the induction agent (basiliximab, alemtuzumab, and thymoglobulin). Protocoled 4 months and 48 months kidney allograft function evaluations with iothalamate clearance test were compared among the three groups. RESULTS: Patients who received basiliximab were significantly older with no difference among African American and Caucasians. The 48 months assessment showed significant decline in median iothalamate clearance in basiliximab group at 49 mL/min vs. alemtuzumab group 64.8 mL/min and thymoglobulin 60 mL/min with P = 0.007. The basiliximab group developed a significant higher proteinuria measured by spot urine to creatinine ratio at 48 months. CONCLUSIONS: The use of basiliximab as an induction agent for kidney transplant is associated with significant decline in kidney function 4 years post transplantation when measured by iothalamate clearance.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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) | 0.001 | 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".