Bibliographic record
Abstract
A systematic review of medical reports produced only one case of a patient being harmed by the internet, reported Anthony Crocco of Montreal Children's Hospital at last week's meeting. Crocco and his contributors began their study in response to the huge amount of publicity given to the harm that might be done to people by information about health on the internet that was wrong, incomplete, or impossible to understand. They expected to find many cases of harm. But having conducted a sophisticated search of five databases, including Medline and Embase, they found only one case—of a patient with lung cancer who had ordered a drug through the internet and died from taking it. They did find eight papers describing self injury resulting from accurate information on the internet, but the intention had existed before the internet was accessed. Surprised by their results, Crocco and others wondered whether that meant that the internet had not caused harm, their search had been inadequate, or studies reporting harm had simply not been published. Crocco is, however, a snowboarder, and he was able to find many reports of harm resulting from snowboarding. Some in the audience suggested that the study reflects the fact that anxiety surrounding the internet is just like the anxiety that surrounds much that is new, including videos, computer games, and—years ago—bicycles and books. Another member of the audience said that with 50-100 million people using the internet and half looking for health information at some time it was inconceivable that both benefits and harms had not resulted. The important question was to measure both the benefits and the harms.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.007 |
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; both teacher heads agree on what is shown here.
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".