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Record W2032935102 · doi:10.4061/2011/203579

Tranexamic Acid Treatment of Life-Threatening Hematuria in Polycystic Kidney Disease

2011· article· en· W2032935102 on OpenAlexaff
Turki AlAmeel, Michael L. West

Bibliographic record

VenueInternational Journal of Nephrology · 2011
Typearticle
Languageen
FieldMedicine
TopicPediatric Urology and Nephrology Studies
Canadian institutionsDalhousie UniversityWestern University
Fundersnot available
KeywordsTranexamic acidMedicineAntifibrinolyticSurgeryKidney diseaseFibrinAnemiaInternal medicineBlood loss

Abstract

fetched live from OpenAlex

A 41-year-old woman with autosomal dominant polycystic kidney disease had chronic kidney disease class IV. She presented 10 days postpartum with a 4-day history of severe hematuria, left flank pain, and anemia, hemoglobin 62 g/L. CT scan showed massively enlarged kidneys with multiple cysts; several cysts bilaterally had high attenuation consistent with hemorrhage. Hematuria persisted over several days despite intensive conservative measures that included vitamin K1, 4 units of plasma, transfusion of 10 units of packed RBCs, Darbopoeitin, and DDAVP. Antifibrinolytic therapy was given with tranexamic acid 1000 mg p.o. t.i.d for one day then OD. The hematuria stopped within 24 hours and did not recur after tranexamic acid therapy ended. Over the next 4 years there were 3 hospitalizations each with severe gross hematuria requiring blood transfusion for acute anemia. The hematuria responded well to further treatment with tranexamic acid. Tranexamic acid produces antifibrinolytic effects via complex interactions with plasminogen, displacing plasminogen from the fibrin surface. Chronic renal impairment is considered a relative contraindication to use of tranexamic acid due to reports of ureteric clots and acute renal failure from cortical necrosis. We conclude that tranexamic acid can be used safely in some patients with CKD and polycystic kidney disease to treat severe hematuria.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.116
Threshold uncertainty score0.441

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.033
GPT teacher head0.283
Teacher spread0.249 · 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.

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

Citations24
Published2011
Admission routes1
Has abstractyes

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