The Magic Touch: A Case Report of How Smartphone Fingerprint Technology Was Utilized to Establish a Last Known Well Time for Recanalization Treatment in a Patient With Acute Ischemic Stroke
Bibliographic record
Abstract
Tissue plasminogen activator (tPA) is currently a standard of care for acute stroke patients. One of the necessary criteria in determining eligibility for tPA is the last known well (LKW) time. The LKW time is unfortunately often difficult to obtain accurately if no witness is available, thus posing as an obstacle for acute recanalization therapy. We present the case of a patient who arrived unresponsive with an unwitnessed onset of symptoms concerning for an acute stroke. An LKW time was able to be successfully established by using her fingerprint to unlock her phone and discover a coherent text sent a few hours prior. Patient was able to receive intravenous (IV) tPA and demonstrated remarkable recovery. The use of fingerprint ID to unlock the patient's phone raises the concern of breach of privacy and whether involuntary smartphone searches apply to the emergency code of conduct outlined by the FDA. Smartphone applications, such as Apple iOS "Medical ID" argues for maximal utilization of smartphone technology for emergent medical conditions. Utilization of smartphone technology can potentially serve a potential solution, but the question remains as to whether this practice would be deemed to be ethically appropriate under the policy of implied informed consent under emergent conditions.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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".