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Record W4389746035 · doi:10.1177/20543581231215849

Case Report of Renal Calculi in a Child Receiving Imatinib for Acute Lymphoblastic Leukemia

2023· article· en· W4389746035 on OpenAlexaff
Alaa Bamahmud, Mohamed Abou El‐Ghar, Roman Jednak, Karl Muchantef, Sharon Abish, David Mitchell, Catherine Vézina, Indra R. Gupta

Bibliographic record

VenueCanadian Journal of Kidney Health and Disease · 2023
Typearticle
Languageen
FieldMedicine
TopicChronic Myeloid Leukemia Treatments
Canadian institutionsMcGill UniversityMontreal Children's Hospital
Fundersnot available
KeywordsMedicineImatinibInternal medicineKidney stonesGastroenterology

Abstract

fetched live from OpenAlex

Rationale: Imatinib is used in the treatment of Philadelphia chromosome positive (Ph+) leukemias and has been reported to have a direct effect on bone physiology. Presentation: To report on a child with Ph+ acute lymphoblastic leukemia who presented with bilateral flank pain and gross hematuria. Diagnosis: She was diagnosed with obstructive kidney stones 101 days after commencing daily oral imatinib. Stone analysis revealed the presence of calcium phosphate. Interventions and outcome: The patient passed the stones spontaneously with medical therapy that included the use of thiazide, allopurinol, and potassium citrate, but she required temporary insertion of a double-J stent to relieve an obstruction. Novel findings: Imatinib inhibits receptor tyrosine kinases and stimulates the flux of calcium from the extracellular fluid into bone, resulting in hypocalcemia with a compensatory rise in parathyroid hormone that may result in phosphaturia and the formation of calcium phosphate stones. Given that kidney stones are rare events in children, we believe that monitoring for kidney stone formation needs to be performed in children receiving imatinib.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
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 score0.008
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0040.003
Science and technology studies0.0040.002
Scholarly communication0.0020.003
Open science0.0020.003
Research integrity0.0080.006
Insufficient payload (model declined to judge)0.0030.001

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 head0.020
GPT teacher head0.308
Teacher spread0.288 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations1
Published2023
Admission routes1
Has abstractyes

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Same venueCanadian Journal of Kidney Health and DiseaseSame topicChronic Myeloid Leukemia TreatmentsFrench-language works237,207