Bibliographic record
Abstract
PURPOSE OF REVIEW: Critical care physicians frequently try to manipulate the preload of the heart to optimize cardiac function. There is, however, still debate as to what actually indicates the preload of the heart. RECENT FINDINGS: Although central venous pressure (CVP) is commonly used to estimate cardiac filling, it is often argued that it is a poor indicator of preload. This is likely true if one does not understand what preload is, principles of measurement with fluid filled systems, the effect of respiratory efforts on the measurement, the physiological determinants of CVP, and finally which point on the tracing to use as the estimate of the preload of the heart. When these are considered, however, the value of the CVP at the base of the 'c' wave gives a good indication of cardiac preload and a value which can be followed. SUMMARY: When properly measured CVP can be a useful guide to the filling status of the right ventricle. CVP is especially useful when followed over time and combined with a measurement of cardiac output. Importantly, preload is only one of the factors determining cardiac output and it must be integrated into a comprehensive approach that takes into account changes in cardiac function and the return of blood to the heart. Finally, the specific value of preload does not indicate volume responsiveness.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".