Bibliographic record
Abstract
This chapter will discuss some consequences of the hepatic hemodynamic disturbances that accompany portal hypertension. Fibrosis or other mechanisms that lead to a restriction of blood flow through the liver have enormous consequences to the entire cardiovascular system, endocrine processing system, and metabolic homeostasis in general. Many systems have evolved to compensate for the disturbances that result from portal hypertension but some regulatory systems result in a worsening of homeostasis as a result of confusion at the afferent end of the signaling process. For example, does the liver respond to a decrease in portal blood flow the same if the decrease is caused by vasoconstriction of the superior mesenteric artery and subsequent reduction in intestinal blood flow as it does if blood flow to the hepatic parenchymal cells is reduced because of portacaval shunt formation? This chapter represents a conceptual focus on our own areas of expertise with no attempt to provide a detailed literature review. Although the references arc few, more detailed references are available in the cited reviews and original articles. The approach taken is to evaluate the effects of increases and decreases in intra-hepatic portal flow on four vital hepatic areas: the hepatic blood reservoir, hepatic arterial blood flow, the hepatorenal reflex, and liver cell mass. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".