Distributed manufacturing of an open-source tourniquet testing system
Bibliographic record
Abstract
Tourniquets are effective for casualty-prevention in emergency situations. The use of centrally-manufactured commercial tourniquets, however, is not always possible due to supply chain disruptions. The open-source hardware model has been applied to overcome these disruptions in humanitarian crises and several low-cost digitally manufacturable open-source tourniquets have been developed. With the low reliability of improvised tourniquets, it is important to ensure that distributed manufacturing of tourniquets is effective and safe. Tourniquets can be tested, but existing tourniquet testers are expensive, bulky, and complex to operate, which limits their accessibility to an even greater extent than tourniquets in extreme settings. This article fulfills a need by providing a small, transportable, open-source additive-manufactured tourniquet tester that enables inexpensive and accurate testing of tourniquets against known clinical parameters. The <$100 tourniquet tester is validated and tested for operating force of tourniquets in the field or in distributed manufacturing facilities. The tourniquet tester has a significant economic and operational advantage compared to proprietary counterparts available on the market. Once calibrated with a blood pressure monitor, the built-in LCD displays the measuring range of the tester as 0 to 200 N, which is enough to test the validation of all tourniquets.
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 | 0.002 | 0.003 |
| 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 | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.005 |
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".