Bibliographic record
Abstract
PURPOSE OF REVIEW: The aim of this article is to review mechanisms of action of vasopressin and clinical studies of vasopressin in septic shock. RECENT FINDINGS: Arginine vasopressin is an important stress hormone that has both vasoactive and antidiuretic properties. The vasoactive properties of vasopressin have been more applicable clinically because of the discovery by Landry and colleagues that there is a deficiency of vasopressin in septic shock and that infusion of relatively low doses of vasopressin improves responsiveness to infused catecholamines (such as norepinephrine). There are at least 16 clinical studies of infusion of vasopressin in patients who have septic shock. The majority of studies found that vasopressin infusion increased blood pressure and urine output, and decreased the dose requirement of norepinephrine. Several studies showed that vasopressin infusion increased urine output. Both vasopressin and norepinephrine have important adverse effects including decreased cardiac output, decreased heart rate, arrhythmias, myocardial ischemia, mesenteric ischemia, and digital ischemia. SUMMARY: It is still unclear whether there is net benefit from low dose vasopressin infusion in patients who have septic shock. There may be certain patients who benefit but there are few studies of a prolonged vasopressin infusion to determine which patients benefit.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".