Spinal and cranial contributions to total cerebrospinal fluid transport
Bibliographic record
Abstract
In this study, we quantified cerebrospinal fluid (CSF) transport from the cranial and spinal subarachnoid spaces separately in sheep and determined the relative proportion of total CSF drainage that occurred from both CSF compartments. Cranial and spinal CSF systems were separated by placement of an extradural ligature over the spinal cord between C(1) and C(2). In one approach, two different radiolabeled human serum albumins (HSA) were introduced into the appropriate CSF compartment by a perfusion system (method 1) or as a bolus injection (method 2). Plasma tracer recoveries in conjunction with a mass balance equation were used to estimate CSF transport. In method 3, catheters connected to reservoirs filled with artificial CSF were introduced into the cranial and spinal CSF compartments. Incremental CSF pressures were established in each CSF system, and the corresponding steady-state flow rates were measured. Total CSF drainage ranged from 0.51 to 0.75 ml. h(-1). cmH(2)O(-1). Expressed as a percentage of the total CSF transport, the ratios of cranial-to-spinal clearance estimated from methods 1, 2, and 3 were 75:25, 88:12, and 75:25, respectively. Primarily on the basis of the data derived from methods 1 and 3, we conclude that the spinal subarachnoid compartment has an important role in CSF clearance and is responsible for approximately one-fourth of total CSF transport.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".