Problems and Risks of Unsolicited E-mails in Patient-Physician Encounters in Travel Medicine Settings
Bibliographic record
Abstract
BACKGROUND: International travel and use of modern information technology are expressions of modern life style. Seeking on-line travel health advice via E-mail for preventive (teleprevention) or diagnostic reasons may become increasingly popular among patients with financial resources and Internet access. This study was undertaken to compare the behavior of travel clinic or tropical medicine physicians and other providers of travel-related medical information services toward unsolicited E-mails from fictitious patients in pretravel and post-travel scenarios. We also wanted to test the potential of E-mail advice for preventive medicine (teleprevention), and to find out how the "Good Samaritan Law" is observed. METHODS: Two different E-mails were posted to E-mail addresses of 171 physicians (members of travel health and/or tropical medicine societies) and services offering advice on travel health issues identified by an AltaVista search. These E-mails, from two different fictitious travelers, were asking for advice regarding malaria prophylaxis in a pretravel scenario and describing symptoms suggesting acute malaria. RESULTS: Of the contacted addresses 43.3% and 49.7% respectively, replied to the pre- and post-travel E-mail. Of those suggesting antimalarial chemoprophylaxis in the pretravel scenario, 13.2% proposed inadequate regimens, and at least 3.5% of the post-travel replies were inappropriate. The "Good Samaritan Law" was observed by a significant number of physicians. CONCLUSION: Both patients and physicians have to be aware of the limitations of E-mail communication. Guidelines protecting physicians against legal and ethical consequences of this new communication technology are urgently needed.
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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 | 0.005 | 0.056 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".