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Record W4229810324 · doi:10.1177/152692480101100409

Reducing Adverse Effects of Immunosuppressive Agents in Kidney Transplant Recipients

2001· review· en· W4229810324 on OpenAlexaff
Ziba Aalamian

Bibliographic record

VenueProgress in Transplantation · 2001
Typereview
Languageen
FieldMedicine
TopicRenal Transplantation Outcomes and Treatments
Canadian institutionsMcGill University Health Centre
Fundersnot available
KeywordsMedicineBasiliximabDaclizumabAdverse effectDiscontinuationSirolimusImmunosuppressionTransplantationMycophenolic acidCalcineurinDiabetes mellitusTacrolimusKidney transplantationRegimenInternal medicineEndocrinology

Abstract

fetched live from OpenAlex

Success in solid organ transplantation with minimal complications can now be achieved for most patients, and a remarkable rate of graft and patient survival can also be expected. However, the potential for adverse events and comorbid conditions increases with longer graft survival. Although the immunosuppressive regimen is central to the outcome of the transplant recipient and directly impacts the survival of the graft, chronic use of immunosuppressive agents is associated with metabolic disturbances such as hypertension, hyperlipidemia, loss of bone density, nephrotoxicity, and diabetes, which may contribute to other comorbid conditions. In addition, changes in appearance, gingival hyperplasia, hirsutism, alopecia, and weight gain disrupt quality of life and may lead to noncompliance with the immunosuppressive regimen. New immunosuppressive medications, including mycophenolate mofetil, sirolimus, basiliximab, and daclizumab, have allowed for experimentation with new regimens designed to reduce or allow discontinuation of corticosteroids and calcineurin inhibitors. This review highlights the impact and cost of immunosuppressive side effects and the potential for new immunosuppressive regimens to reduce this substantial clinical burden in transplantation.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.636
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.038
GPT teacher head0.365
Teacher spread0.327 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designSystematic review
Domainnot available
GenreReview

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".

Quick stats

Citations8
Published2001
Admission routes1
Has abstractyes

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