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Record W2415761903 · doi:10.5858/2002-126-0753-ug

Urinary Gems

2002· article· en· W2415761903 on OpenAlexaffabout
Andrew W. Lyon, Adnan Mansoor, Martin J. Trotter

Bibliographic record

VenueArchives of Pathology & Laboratory Medicine · 2002
Typearticle
Languageen
FieldMedicine
TopicCytomegalovirus and herpesvirus research
Canadian institutionsCalgary Laboratory ServicesUniversity of Calgary
Fundersnot available
KeywordsUrinalysisUrineHypokalemiaMedicineChemotherapyGastroenterologyInternal medicine

Abstract

fetched live from OpenAlex

A 53-year-old woman with a history of metastatic breast cancer was admitted 12 days following her sixth cycle of capecitabine and docetaxel chemotherapy because of hypokalemia, hypocalcemia, and dehydration. Prolonged diarrhea followed the chemotherapy and may have contributed to the development of hypokalemia. The chemotherapy protocol was suspended, and the patient received fluid support, potassium, and calcium carbonate supplements. Urine microscopy detected yeast, subsequently identified as Candida glabrata by urine culture, and the patient was treated with fluconazole.Four days later, this immunosuppressed patient developed a sore swollen tongue. An oral infection by herpes simplex virus was presumed, and treatment with acyclovir was initiated as follows: day 1, 200 mg oral every 5 hours; days 2 through 4, 400 mg intravenous every 8 hours. On the fourth day of acyclovir treatment, a random urine specimen was submitted for chemical and microscopic urinalysis. The urine was cloudy and yellow with a high specific gravity level (>1.030), low pH (5.5), and a glucose level of 0.1 g/dL (5.5 mmol/L); there was a strong reaction for blood (+++) and protein (100 mg/dL), but no reaction for ketones, nitrite, or leukocyte esterase (Multistix 8 SG, Bayer Inc, Etobicoke, Ontario, Canada). Microscopy of the urine sediment revealed abundant, colorless, transparent, fine-needle–shaped crystals and a few red blood cells and yeast cells (Figure 1). The crystals had either sharp ends or blunt ends and had red-green birefringence in polarized light (Figure 2). The large quantity of crystals caused the cloudy appearance of the fluid and suggested radiographic contrast material or drug-associated crystalluria. The laboratory medical staff was consulted when the ward staff denied that the patient had had recent radiology studies or antibiotic therapy. Following review of pharmaceutical use and patient history, the possibility of acyclovir crystalluria was considered likely.This case presented 2 teaching points. First, acyclovir crystalluria is a rare side effect of a very commonly used drug. While there are few published reports that specifically describe this crystalluria, acyclovir-induced renal failure was observed in 58 of 354 patients following intravenous drug administration.1 It is surprising that acyclovir crystalluria is seldom observed. Drug-induced crystalluria is frequently observed in tertiary-care centers, and it is important to remember that prompt attention to this urine microscopy observation can help avoid drug-associated renal toxicity.The second teaching point was that desktop electronic access to medical literature had a positive influence on this patient's care. A quick search of the National Library of Medicine's PubMed database for acyclovir crystalluria identified 4 previous reports and also indicated that acyclovir treatment has a risk of nephrotoxicity due to renal tubular damage by crystals.2–5 The shape and properties of the crystals we observed were consistent with acyclovir but were not sufficiently unique to allow identification.2–4 The suspicion of acyclovir crystalluria was noted on the urinalysis report and in consultation with the attending physician. Two days later, the patient's medical chart contained a review article on drug-induced crystalluria and renal failure obtained via electronic access to medical journals by the oncology staff; acyclovir treatment for this patient was discontinued and the crystalluria resolved within 24 hours with no indication of renal toxicity based on the creatinine levels. This scenario of using desktop access to medical literature to rapidly and conveniently research unusual observations is becoming routine practice among many pathologists, clinicians, and patients.

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)0.004
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.019
Threshold uncertainty score0.064

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.003
Science and technology studies0.0020.001
Scholarly communication0.0020.002
Open science0.0010.002
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0190.006

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 head0.035
GPT teacher head0.312
Teacher spread0.277 · 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

Citations14
Published2002
Admission routes2
Has abstractyes

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