A Q&A with the Qualcomm Tricorder XPRIZE Winners
Bibliographic record
Abstract
In recent years, there has been an increasing demand for “health consumer technologies”—small, user-friendly, point-of-care devices that can be operated by untrained individuals to evaluate health and disease. The Qualcomm Tricorder XPRIZE was a $10 million global competition to stimulate innovation and integration of advanced technologies, enabling reliable health diagnoses anywhere and anytime (https://tricorder.xprize.org/). The competition called for the development of a device that could diagnose 12 diseases (and the absence of disease) and capture 5 real-time health vital signs independent of a healthcare professional or facility. Devices also could not weigh >5 pounds and required the capability to transmit data to a cloud storage and computing system. Although diagnostic accuracy was a key component, the competition was unique in its strong emphasis on user adoption and experience. In fact, only teams scoring the highest on the consumer experience evaluations were eligible to win the overall competition. After the competition's launch in 2012, >300 teams joined. In April 2017, 3 winners were announced. Final Frontier Medical Devices, led by Basil Harris, was announced the highest performing team and received $2.6 million for their achievement. Dynamical Biomarkers Group, led by Chung Kang Peng, received $1 million for second place. Cloud DX, led by Sonny Kohli, was also recognized as XPRIZE's first Bold Epic Innovator and received $100000.
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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 | 0.009 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.021 | 0.008 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.008 | 0.007 |
| Insufficient payload (model declined to judge) | 0.673 | 0.568 |
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".