Bibliographic record
Abstract
PURPOSE OF REVIEW: Autoregulation of renal blood flow has traditionally been considered to stabilize glomerular filtration, and thus tubular load, in the face of blood pressure fluctuations. This view arose because of the contribution of tubuloglomerular feedback, which senses distal tubular fluid composition, to regulation and autoregulation of renal blood flow. Studies have indicated a more important role for the myogenic mechanism. It has been proposed that the 'purpose' of autoregulation is to defend glomerular structure. Both these views may be incomplete because neither takes into consideration the complex interactions between tubuloglomerular feedback and the myogenic mechanism and among nephrons whose afferent arterioles derived from a common interlobular artery. RECENT FINDINGS: Recent findings indicate that it is now indisputable that effective autoregulation is necessary for defense of glomerular structure. Extensive modulation of the myogenic mechanism by tubuloglomerular feedback has been shown using a variety of experimental designs that have illuminated one pathway (neuronal nitric oxide synthase at the macula densa) by which this occurs. SUMMARY: These findings indicate that the myogenic mechanism can no longer be considered as a purely vascular mechanism in the kidney and instead receives information via tubuloglomerular feedback about the status of renal function.
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 | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| 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".