A man with rust coloured urine and normocytic anaemia
Bibliographic record
Abstract
A 65 year old man presented to the emergency department with a 10 day history of dark urine, general malaise, and progressive shortness of breath. He had a history of rheumatic heart disease and had been on warfarin since he had a metallic mitral valve replacement in 1984. He was a retired teacher, drank a minimal amount of alcohol, and was a non-smoker. He had no family history of cancer. On examination he was jaundiced with a sinus tachycardia (120 beats/min) and a pansystolic murmur. His other vital signs were normal. On examination, his other systems were normal except for mild suprapubic tenderness, as were the results of a digital rectal examination. His urine was rust coloured (fig 1⇓) with no blood clots. Urine dipstick was positive for blood (3+), protein (3+), and leucocytes (3+), but negative for nitrites. No red blood cells or casts were noted during microscopy. Fig 1 Patient’s urine on admission The table⇓ shows the results of his initial laboratory investigations. A peripheral blood film showed red cell fragments, polychromasia, and normal platelets (a representative smear is shown in fig 2⇓). The results of chest radiography, computed tomography of the kidneys, and ultrasound of the urinary tract were also unremarkable. Serial sepsis screens and three sets of blood cultures for endocarditis were all negative. View this table: Laboratory test results on admission Fig 2 A representative blood film showing red cell fragments (arrows) ### 1. What are the causes of dark urine? #### Answer Haematuria, menstrual contamination, haemoglobinuria, and myoglobinuria will cause dark urine that is positive for blood on urine dipstick testing. Red cells …
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".