Adverse Consequences of Internet Purchase of Pharmacologic Agents or Dietary Supplements
Bibliographic record
Abstract
Background: The Internet is commonly used to advertise and sell medications and dietary supplements directly to consumers. Prescriptions are often not needed, and consumers may engage in unmonitored and risky health practices. No systematic attempt has been made to evaluate reported cases of adverse events following such purchases. Objective: To systematically identify and examine reported cases of adverse events associated with the purchase of medications and dietary supplements from the Internet. Methods: MEDLINE (1990-June 2009), EMBASE (1990-June 2009), IBIDS (to June 2009), TOXNET (to June 2009), bibliographies of identified articles, and Web sites of relevant health ministries and professional associations in the US and Canada were reviewed to identify eligible articles that describe adverse events associated with the purchase of medications or dietary supplements from the Internet. Results: Thirty-two reports of 41 cases of adverse consequences of pharmaceutical products (n = 31) or dietary supplements (n = 10) were identified. Purchases were made by people in the 30- to 50-year-old age group in 36% of cases. Prescription medications were implicated in 27% of cases and narcotic and controlled drugs were implicated in 49% of cases. Drug abuse was responsible for harm in 73% of cases, whereas adverse drug reactions occurred in 27% of cases. Nine (22%) patients died as a result of adverse consequences following such purchases. The remaining patients suffered serious adverse events such as seizures, liver damage, and hallucinations. Conclusions: An unexpectedly large number of case reports were identified from the literature; however, these reports do not fully illustrate the magnitude of the problem. Life-endangering adverse consequences signify a need for increased regulation and control of Internet Web sites and a need for healthcare provider involvement. Pharmacists should know where their patients obtain medications, how to verify the validity of the sources of prescriptions, and how to report adverse consequences.
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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 | 0.001 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".