Wireless-local-area-network deployment issues in hospitals: Capacity, coverage & electromagnetic compatibility
Bibliographic record
Abstract
Healthcare is an important industry that touches most, if not all, of us. The wireless communication revolution can potentially provide medical staff with rapid, bedside access to medical information. Rapid access not only improves healthcare delivery, but can also reduce medical errors, which have been estimated to cause up to 100,000 patient deaths each year in US hospitals alone [1]. Given the potential benefits of using wireless informatics in hospitals, some hospitals are currently using wireless local area network (WLAN) informatics systems, and many hospitals would like to. The potentially-enormous benefit of WLAN informatics comes with the concern that electromagnetic interference (EMI) from radio-frequency (RF) sources might cause critical-care medical equipment to malfunction. There have been many previous reports of medical device malfunction due to EMI [2,3]. Many EMI sources were described, including, although relatively rarely [2], wireless LANs.
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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 | 0.001 |
| 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".