Searching for medical information online: a survey of Canadian nephrologists
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND: Physicians often search for information to improve patient care. We evaluated how nephrologists use online information sources for this purpose. METHODS: In this cross-sectional study (2008 to 2010), a random sample of Canadian nephrologists completed a survey of their online search practices. We queried respondents on their searching preferences, practices and use of 9 online information sources. RESULTS: Respondents (n=115; 75% response rate) comprised both academic (59%) and community-based (41%) nephrologists. Respondents were an average of 48 years old and were in practice for an average of 15 years. Nephrologists used a variety of online sources to retrieve information on patient treatment including UpToDate (92%), PubMed (89%), Google (76%) and Ovid MEDLINE (55%). Community-based nephrologists were more likely to consult UpToDate first (91%), while academic nephrologists were divided between UpToDate (58%) and PubMed (41%). When searching bibliographic resources such as PubMed, 80% of nephrologists scan a maximum of 40 citations (the equivalent of 2 search pages in PubMed). Searching practices did not differ by age, sex or years in practice. CONCLUSIONS: Nephrologists routinely use a variety of online resources to search for information for patient care. These include bibliographic databases, general search engines and specialized medical resources.
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.
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 | 0.009 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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 it