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Record W2015957773 · doi:10.1371/journal.pone.0059963

Analysis of Free Online Physician Advice Services

2013· article· en· W2015957773 on OpenAlexaboutno aff
Raphael Cohen, Michael Elhadad, Ohad S. Birk

Bibliographic record

VenuePLoS ONE · 2013
Typearticle
Languageen
FieldHealth Professions
TopicHealth Literacy and Information Accessibility
Canadian institutionsnot available
Fundersnot available
KeywordsAdvice (programming)Scope (computer science)Medical educationMedicineWorld Wide WebComputer scienceFamily medicine

Abstract

fetched live from OpenAlex

BACKGROUND: Online Consumer Health websites are a major source of information for patients worldwide. We focus on another modality, online physician advice. We aim to evaluate and compare the freely available online expert physicians' advice in different countries, its scope and the type of content provided. SETTING: Using automated methods for information retrieval and analysis, we compared consumer health portals from the US, Canada, the UK and Israel (WebMD,NetDoctor,AskTheDoctor and BeOK). The evaluated content was generated between 2002 and 2011. RESULTS: We analyzed the different sites, looking at the distribution of questions in the various health topics, answer lengths and content type. Answers could be categorized into longer broad-educational answers versus shorter patient-specific ones, with different physicians having personal preferences as to answer type. The Israeli website BeOK, providing 10 times the number of answers than in the other three health portals, supplied answers that are shorter on average than in the other websites. Response times in these sites may be rapid with 32% of the WebMD answers and 64% of the BeOK answers provided in less than 24 hours. The voluntary contribution model used by BeOK and WebMD enables generation of large numbers of physician expert answers at low cost, providing 50,000 and 3,500 answers per year, respectively. CONCLUSIONS: Unlike health information in online databases or advice and support in patient-forums, online physician advice provides qualified specialists' responses directly relevant to the questions asked. Our analysis showed that high numbers of expert answers could be generated in a timely fashion using a voluntary model. The length of answers varied significantly between the internet sites. Longer answers were associated with educational content while short answers were associated with patient-specific content. Standard site-specific guidelines for expert answers will allow for more desirable content (educational content) or better throughput (patient-specific content).

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 imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.038
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.014
Threshold uncertainty score0.045

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.038
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0060.006
Science and technology studies0.0010.000
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0140.002

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.

Opus teacher head0.068
GPT teacher head0.392
Teacher spread0.323 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations9
Published2013
Admission routes1
Has abstractyes

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