The Importance of Health Information on the Internet: How It Saved My Life and How it Can Save Yours (Preprint)
Bibliographic record
Abstract
UNSTRUCTURED The Internet holds the promise of helping to lead to improved patient outcomes, especially when one is faced with a critical or life-threatening disease or condition. Appropriate and timely access to health information can support more informed negotiation of optimal treatments, optimal management and expedited recovery and ultimately an improved patient outcome. However, there are many human and technical barriers that may prevent or hinder the application of the best possible information for both patient and provider alike, making the patient journey complex and potentially dangerous. In this editorial the author reflects on a personal patient journey where use of the Internet facilitated a means to reach a good patient outcome in the face of a variety of informational and organizational limitations and gaps. The journey illustrates the importance of human related factors affecting access to health information. The application of a range of Internet information resources, applied at critical points can result in a positive patient outcome, as the case illustrates. This editorial reflects on how the experience highlights a number of information needs and concerns. It also highlights the need for improved access to appropriate health information along the patient journey that can support patient and provider joint decision making. This access to information can literally make the difference between positive clinical outcomes and death, illustrating how health information on the Internet can be both critical and life saving.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.035 | 0.017 |
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".