Bibliographic record
Abstract
(Section Editor: K. Kühn) It is not uncommon for more than one electrolyte and/or acid base disorder to be present simultaneously. In such a setting, determining the aetiological and pathophysiological factors involved can represent a real challenge. Failure to identify these factors thoroughly may lead to a disastrous outcome either from improper fluid and electrolyte management or from the ongoing consequence of an underlying illness left untreated. Here, we report the case of a patient who developed pronounced hyponatraemia, metabolic acidosis and hyperazotaemia. These manifestations were associated with true volume depletion and a severe decline in urine output. Rapid correction of plasma Na (PNa) was avoided by recognizing the important role played by hypovolaemia and high plasma urea (Purea) in free water excretion. In addition, a definitive cure could be offered to the patient after identifying the underlying illness responsible for the clinical manifestations. On March 21, 2000, a nephrological evaluation was requested for a 53‐year‐old woman with hyponatraemia and decreased consciousness. Her main past medical history consisted of: (i) an adenocarcinoma of the uterus in 1994, treated by hysterectomy and radiotherapy; (ii) a Dukes 2A adenocarcinoma of the colon in 1998, treated by total colectomy with terminal ileostomy; and (iii) a rectocutaneous fistula that closed spontaneously in 1999.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.009 | 0.008 |
| Insufficient payload (model declined to judge) | 0.004 | 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".