Does Accidental Overcorrection of Symptomatic Hyponatremia in Chronic Heart Failure Require Specific Therapeutic Adjustments for Preventing Central Pontine Myelinolysis?
Bibliographic record
Abstract
This review aims at summarizing essential aspects of epidemiology and pathophysiology of hyponatremia in chronic heart failure (CHF), to set the ground for a practical as well as evidence-based approach to treatment. As a guide through the discussion of the available evidence, a clinical case of hyponatremia associated with CHF is presented. For this case, the severe neurological signs at presentation justified an emergency treatment with hypertonic saline plus furosemide, as indicated. Subsequently, as the neurological emergency began to subside, the reversion of the trend toward hyponatremia overcorrection was realized by continuous infusion of hypotonic solutions, and administration of desmopressin, so as to prevent the very feared risk of an osmotic demyelination syndrome. This very disabling complication of the hyponatremia correction is then briefly outlined. Moreover, the possible advantages related to systematic correction of the hyponatremia that occurs in the course of CHF are mentioned. Additionally, the case of tolvaptan, a vasopressin receptor antagonist, is concisely presented in order to underline the different views that have led to different norms in Europe with respect to the USA or Japan as regards the use of this drug as a therapeutic resource against the hyponatremia.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".