Cerebrospinal fluid volume measurements in hydrocephalic rats
Bibliographic record
Abstract
Object Experimental data about the evolution of intracranial volume and pressure in cases of hydrocephalus are limited due to the lack of available monitoring techniques. In this study, the authors validate intracranial CSF volume measurements within the lateral ventricle, while simultaneously using impedance sensors and pressure transducers in hydrocephalic animals. Methods A volume sensor was fabricated and connected to a catheter that was used as a shunt to withdraw CSF. In vitro bench-top calibration experiments were created to provide data for the animal experiments and to validate the sensors. To validate the measurement technique in a physiological system, hydrocephalus was induced in weanling rats by kaolin injection into the cisterna magna. At 28 days after induction, the sensor was implanted into the lateral ventricles. After sealing the skull using dental cement, an acute CSF drainage/infusion protocol consisting of 4 sequential phases was performed with a pump. Implant location was confirmed via radiography using intraventricular iohexol contrast administration. Results Controlled CSF shunting in vivo with hydrocephalic rats resulted in precise and accurate sensor measurements (r = 0.98). Shunting resulted in a 17.3% maximum measurement error between measured volume and actual volume as assessed by a Bland-Altman plot. A secondary outcome confirmed that both ventricular volume and intracranial pressure decreased during CSF shunting and increased during infusion. Ventricular enlargement consistent with successful hydrocephalus induction was confirmed using imaging, as well as postmortem. These results indicate that volume monitoring is feasible for clinical cases of hydrocephalus. Conclusions This work marks a departure from traditional shunting systems currently used to treat hydrocephalus. The overall clinical application is to provide alternative monitoring and treatment options for patients. Future work includes development and testing of a chronic (long-term) volume monitoring system.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".