A Mother and Newborn with Brown Blood
Bibliographic record
Abstract
A 33-year-old woman of Indian origin presented for prenatal care at term. An ultrasound report from earlier in her pregnancy indicated complete placenta previa and placenta increta, which were confirmed by repeat ultrasound. She was admitted to the hospital for monitoring and delivery planning. On admission, brown vaginal spotting was noted on the patient's pad. Her nail beds and fingers were cyanotic, and a finger prick blood sample was rusty brown in appearance. Oxygen saturation by pulse oximetry on room air was 45% despite her appearing clinically well. She was otherwise asymptomatic. The patient's husband remarked that her family has “brown blood, not red,” noting that the patient's father, paternal grandfather, and sister also have brown blood but no clinical problems. Her 3-year-old daughter had a similar cyanotic appearance but was otherwise asymptomatic. Repeat oxygen saturation measurements by pulse oximetry consistently gave results between 40%–60%, and arterial blood P o2 was 95 mmHg. Methemoglobin measurement was attempted, but the cooximeter indicated “?Oximetry measuring error.” This was also confirmed by repeated measurements. Complete blood count results were as follows: hemoglobin, 12.5 g/dL (125 g/L; reference interval, 120–160 g/L); red blood cells (RBC),6 4.19 × 1012/L (reference interval, 3.80–5.20 × 1012/L); hematocrit (HCT), 38% (0.38; reference interval, 0.36–0.46); mean corpuscular volume (MCV), 90 fL (reference interval, 80–100 fL); mean corpuscular hemoglobin (MCH), 30 pg (reference interval, 26–35 pg); mean corpuscular hemoglobin concentration (MCHC), 33.3 g/dL (333 g/L; reference interval, 310–360 g/L); red cell width distribution (RDW), 13.2% (reference interval, <15.6%); platelets (PLT), 224 × 109/L (reference interval, 140–450 × 109/L); white blood cells (WBC) 9.6 × 109/L (reference interval, 4.0–11.0 × 109/L). ### QUESTIONS TO CONSIDER 1. What are causes of cyanosis and brown blood? 2. What additional laboratory tests could be useful in …
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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".