A novel implantable blood pressure telemetry device; Comparison between Data Sciences and Telemetry Research systems.
Bibliographic record
Abstract
The pending expiry (May 2008) of a Data Sciences (DSI) patent in the area of blood pressure telemetry permits the development of alternative technologies. A key aspect in providing new telemetry systems is a comparison to existing technology. Important aspects include stability of the calibration over time and the ability to capture the pulsitile blood pressure waveform. In a group of 6 rats and 5 rabbits DSI blood pressure transmitters (C40 or D70 models) were implanted in conjunction with Telemetry Research (TR) transmitters. Both systems incorporate a fluid filled catheter of similar dimensions with a biocompatible gel in the tip. The blood pressure waveform was collected via telemetry for up to 2 months after implantation. The signal was sampled at 500 Hz and digitally transmitted to a receiver up to 5 m away The battery of TR transmitter was recharged within the rat using inductive power transfer technology. The pulsitile waveform associated with each heart beat was reflected similarly in all cases although the frequency response of DSI telemeters was limited to ~40 Hz (−3 dB rolloff point). The calibrated offset level between the two transmitters was not more than 5 mmHg at all times over a 2 month period. We conclude that the Telemetry Research blood pressure transmitters offer comparable performance to existing technology but with extra design advantages (rechargeable, co‐housing of animals, greater range).
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".