Bibliographic record
Abstract
VignetteEM, an 85 year-old female, was admitted to the Medical Teaching Unit with a one-week history of confusion.In the Emergency Department, she was disoriented and later became somnolent.During the month prior to admission, she had experienced progressive mid-back pain, and had been diagnosed with a T8 compression fracture.Laboratory investigations showed a hemoglobin of 81 g/L with mean corpuscular volume of 101 fL.Rouleaux formations were seen on peripheral smear.EM had elevated creatinine (133 mmol/L), urea (11.2 mmol/L), and ionized calcium (1.97 mmol/L); however, parathyroid hormone levels were normal, as were iron studies, vitamin B12, folate, and thyroid stimulating hormone (TSH).Urine culture revealed Escherichia coli bacteriuria, which was treated with ceftriaxone.Pamidronate was administered for hypercalcemia.Early into the admission, she became fluid overloaded and required diuresis, while simultaneously receiving intravenous fluids for her hypercalcemia.Multiple myeloma was considered as the cause of EM's constellation of symptoms, so a serum protein electrophoresis was performed, revealing an IgA monoclonal protein spike.Free light chain analysis showed an increase in free kappa light chains (7.69 mg/L) with a markedly elevated kappa/lambda ratio of 157.5.
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 imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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.
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 source (direct Gemma or distilled Codex), 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".