Analysis of Cases of Harm Associated With Use of Health Information on the Internet
Bibliographic record
Abstract
CONTEXT: There is concern about the potential harm associated with the use of poor quality health information on the Internet. To date, there have been no systematic attempts to examine reported cases of such harm. METHODS: We conducted a systematic review of the peer-reviewed literature, to evaluate the number and characteristics of reported cases of harm associated with the use of health information obtained on the Internet. Using a refined strategy, we searched MEDLINE (from 1966 to February 2001), CINAHL (from 1982 to March 2001), HealthStar (from 1975 to December 2000), PsycINFO (from 1967 to March 2001), and EMBASE (from 1980 to March 2001). This was complemented with searches of reference lists. Two authors separately reviewed the abstracts to identify articles that describe at least 1 case of harm associated with the use of health information found on the Internet. Articles of any format and in any language deemed possibly relevant by either researcher were obtained and reviewed by both researches. RESULTS: The search yielded 1512 abstracts. Of these 186 papers were reviewed in full text. Of these, 3 articles satisfied the selection criteria. One article described 2 cases in which improper Internet searches led to emotional harm. The second article described dogs being poisoned because of misinformation obtained on the Internet. The third article described hepatorenal failure in an oncology patient who obtained misinformation about the use of medication on the Internet. CONCLUSIONS: Despite the popularity of publications warning of the potential harm associated with using health information from the Internet, our search found few reported cases of harm. This may be due to an actual low risk for harm associated with the use of information available on the Internet, to underreporting of cases, or to bias.
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 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 | 0.029 | 0.186 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.047 | 0.025 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".