PROS AND CONS OF TECHNOLOGY FOR PATIENTS
Bibliographic record
Abstract
Introduction: Technology developed specifically for patients progresses quickly and helps patients in hospital as well as at home. It helps the healthy population to stay healthy. Technology can broadly be divided into hardware and software. Main Text: When used under the supervision of health professionals, technology is mostly beneficial - when harm, or no benefit is detected, the technology is withdrawn or corrected. Uncontrolled use of technology without verification and without monitoring of outcomes often leads to negative effects. Without regulation, technology continues to be used even when proven to be useless or even harmful. Conclusion: Uncontrolled use of technology with no input from health professionals, social media, and internet access with unreliable sources has more negative than positive effect. There is need for more research on how to successfully educate patients since technology is quickly expanding, and it is easier than ever to access to information online. Traditional education relying on authority is not currently successful.
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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 | 0.001 | 0.000 |
| 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".