Bibliographic record
Abstract
PURPOSE OF THE REVIEW: Vasoactive drugs are the mainstay of hemodynamic management of vasodilatory shock when fluids fail to restore tissue perfusion. In this review, studies published during the past year that increase our understanding of the use of vasoactive drugs in the intensive care unit are discussed. RECENT FINDINGS: In septic shock, there is no benefit in increasing mean arterial pressure from 65 to 85 mmHg. Norepinephrine did not worsen renal function. Epinephrine induced visceral hypoperfusion and hyperlactatemia, and worsened organ function and survival compared with norepinephrine and vasopressin. There are a number of reports of the safety and efficacy of vasopressin but it is not currently recommended as first line therapy, and if used, should be given as a continuous low dose infusion. Terlipressin is showing promise but decreases cardiac output. Metaraminol is being investigated as an alternative to norepinephrine. Dopamine may improve splanchnic flow mainly by increasing cardiac output. Dobutamine improves oxygen delivery and may improve mesenteric blood flow. SUMMARY: Over the last 40 years, there have been few controlled clinical trials to guide clinicians on the use of vasoactive drugs of treating shock states. It is not known whether the currently favored combination of norepinephrine and dobutamine is superior to traditional therapy with dopamine. Epinephrine is not recommended as the first-line therapy. The role of vasopressin and terlipressin remains unknown. Three large ongoing clinical trials will be completed soon and the results should clarify the role of these various agents.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".