The PolyVent educational platform: An open mechanical ventilation platform for research and education
Bibliographic record
Abstract
The PolyVent is an open source mechanical ventilator meant for research and education. It prioritizes openness, modularity, repairability, and modifiability. An ESP32 microcontroller controls a proportional valve which precisely modulates pressure and flow from a mixing chamber into the airway. This chamber is fed with pressurized oxygen and medical air. Solenoid valves control both gas mixing and patient inflation. The PolyVent is controllable through a command-line interface over the serial port, a convenient point of access for researchers and instructors. The VentMon, a separate IoT-enabled spirometer, provides convenient instrumentation for classroom teaching and geodistributed research teams. A "cake-dome" design allows the PolyVent to operate with or without its transparent cover in place, for easy troubleshooting and instruction. An open footprint optimizes engineering change rather than compactness. The electronics are packaged into cards on a standardized backplane, allowing one to extend functionality through the addition of new cards. The VentOS open source software that drives the machine makes it a universal and modifiable research software platform. It is intended to be the medical gas production heart of an open source human respiration research and education ecosystem, and aims to be the starting point for open source medical ventilator designs.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.050 | 0.019 |
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".