Anesthesiologists' Responses to an Email Request for Advice from an Unknown Patient
Bibliographic record
Abstract
BACKGROUND: People are using the Internet as a method of getting medical advice. Some Web sites include the email addresses of physicians, and some people are contacting these physicians for advice. As many patients undergo surgery on a "day surgery" basis, they often have no opportunity to ask anesthesiologists for advice before surgery; these patients may be more likely than other groups to use Internet email to ask questions. It seemed that it would be useful to find out what, if any, advice anesthesiologists would give in response to email from an unknown patient. OBJECTIVE: To determine how anesthesiologists would respond to an email requesting advice about an anesthetic problem from an unknown patient. METHODS: In February 1998, an email message was sent from a fictitious patient, using an email address created for this study, to 115 anesthesiologists whose email addresses were found on publicly accessible web sites. The message described the patient's problem with a previously administered anesthetic and requested advice about anesthesia for upcoming surgery. Responses were entered in a database and analyzed to determine the percentage of anesthesiologists who responded, and how helpful, accurate, and complete their advice was. RESULTS: Fifty-eight responses were obtained from 108 valid email addresses (54% response rate). Of these, 78% were received within 48 hours. Eighty-three percent (83%) of respondents suggested contacting a local physician, 62% mentioned reviewing the old chart, and 41% suggested a specific diagnosis. None of the initial replies contained inaccurate advice, but only five responses were considered to be comprehensive. Ten percent (10%) included a disclaimer with the response. Eighty-three percent (83%) of replies were subjectively assessed as being friendly in tone. CONCLUSIONS: At present, patients who email an unknown anesthesiologist can expect to get a reply from over half. The advice is likely to be prompt, friendly, and to provide accurate and appropriate--but probably incomplete--advice.
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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 | 0.004 | 0.049 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| 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 | 0.002 |
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".