A SWOT Analysis of the Use and Potential Misuse of Implantable Monitoring Devices by Athletes
Bibliographic record
Abstract
We have been following the developments and popularity of commercially available wearable sensor technology, as well as the ongoing discussion concerning its usefulness for improving the fitness and health of athletes (Düking et al., 2016, 2017; Sperlich and Holmberg, 2017) with considerable interest.\n\nHere, we would like to draw attention to a new generation of implantable devices (implantables) currently being promoted as “the next wave of sensor-based smart devices” (Khosravi, 2015) and predicted to be “a big thing in three years” (Mercer, 2016). We perform a SWOT analysis regarding their use, especially by athletes and in sports, with the goal of identifying internal strengths and weaknesses, as well as external opportunities and threats.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".