Bibliographic record
Abstract
OBJECTIVE: Measurement of blood pressure is fundamental for the management of patients in shock, yet the physiological basis and meaning of blood pressure measurements are complex and often not well understood. This article is in two parts: part 1 deals with the mechanical and physiological aspects of blood pressure and its measurement and part 2 deals with the role of changes in regional resistances in the determination of tissue perfusion and bedside approaches to management of shock. DATA SOURCE AND SELECTION: This review is based on physiological principles from texts and experimental studies which elucidate some of the key principles. The views expressed are the author's synthesis of the views of others and his own opinions. DATA SYNTHESIS: Arterial blood pressure is a major determinant of regional flow and is often used as a surrogate indicator of tissue perfusion, but in reality, it is a poor indicator of blood flow. Blood pressure is determined by cardiac output (total flow) and total vascular resistance. Distribution of flow for a given blood pressure is dependent on the relative values of resistances in different vascular beds. If this distribution of resistances were known, this would be the ideal guide to therapy. Unfortunately, regional resistances cannot be assessed in the clinical setting, and so we are left with blood pressure as a guide to therapy. CONCLUSIONS: This article discusses the implications of these points and explores factors that need to be taken into account when designing empiric trials to determine appropriate blood pressure targets for patients in shock. Even if well-studied empirically developed guidelines become available, it likely still will be important to individualize patient management and approaches for this are discussed too.
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 | 0.013 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.011 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".