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Record W4393951260 · doi:10.1016/j.cpccr.2024.100288

Challenges in managing chronic kidney disease with simultaneous renal transplant immunosuppressant induced buccal squamous cell carcinoma and gastric Burkitt's like lymphoma: A case report

2024· article· en· W4393951260 on OpenAlexafffund
Syeda Sara Tajammul, Shruti Maheshwari, Javeria Munir, Khalil Al‐Farsi, Ali I. Al-Lawati, Zamzam Al Hashami, Layth Mula‐Hussain

Bibliographic record

VenueCurrent Problems in Cancer Case Reports · 2024
Typearticle
Languageen
FieldMedicine
TopicViral-associated cancers and disorders
Canadian institutionsDalhousie University
FundersFaculty of Medicine, Dalhousie UniversitySultan Qaboos University
KeywordsMedicineBuccal administrationBasal cellLymphomaKidney diseaseGastroenterologyCancer researchInternal medicinePathologyOncologyPharmacology

Abstract

fetched live from OpenAlex

Immunodeficiency is associated with higher cancer incidence, especially in transplanted patients; however, it is unknown whether there is a link between immunodeficiency and the development of multiple primary malignancies. Immunosuppressive drugs may either indirectly potentiate the effect of carcinogens or directly damage the DNA. Skin cancers are the most common malignancies diagnosed in renal transplant recipients. Management of immunosuppression in recipients of transplants who are living with cancer is complex and challenging. A concerted approach between transplant professionals, oncologists, and allied health professionals is therefore needed to ensure optimal care for transplant recipients who are developing immunodeficiency-induced malignancies. Here, we report a challenging case that presented with two simultaneous malignancies (buccal squamous cell carcinoma and gastric Burkitt's-like lymphoma) after nine years of being on immunosuppressants after the kidney transplant. The patient tolerated his cancer treatments with some grade II-III toxicities and is currently a two-year disease-free survivor. Focusing on the curative intent approaches for the two cancers with the adjustment of the immunosuppressant medications, besides the complications associated with these radical treatments, is worthy of being presented to the transplant and oncology teams globally.

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: Case report · Consensus signal: Case report
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.220
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
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 head0.027
GPT teacher head0.282
Teacher spread0.255 · 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 designCase report
Domainnot available
GenreEmpirical

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

Citations0
Published2024
Admission routes2
Has abstractyes

Explore more

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