Poor Online Patient Ratings of Otolaryngologists in the United States: What are Patients Saying?
Bibliographic record
Abstract
ObjectivesOnline patient forums have become a platform for patient education and advocacy in many areas of medicine. The anonymity provided by such forums may encourage honest, candid responses. Using patient online reviews, this study sought to explore themes that arose from negatively perceived care interactions with American otolaryngologists using the Accreditation Council for Graduate Medical Education (ACGME) competency framework.Study DesignQualitative thematic analysis.MethodsThrough an iterative multistep process, a qualitative thematic analysis was conducted on negative reviews (defined as ratings of two or less out of five) of all American otolaryngologists found on a popular online physician-rating website (RateMDs.com).ResultsA systematic search through the RateMDs website revealed 2950 separate comments of negative reviews. Of these negative reviews, 350 were randomly selected for thematic analysis. The predominant themes that emerged aligned closely with the Accreditation Council for Graduate Medical Education (ACGME) competencies, in particularly with professionalism and interprofessional skills and communication.ConclusionsThe negative reviews of American otolaryngologists revealed a number of areas where improvements could be made to quality of care. Patients value evidence-based medicine delivered by compassionate and respectful physicians. Isolating and aligning predominant themes within the ACGME framework proved a productive method to collect and organize pertinent patient feedback and integrate teaching into the post-graduate training and continuing professional development in order to avoid such negatively perceived interactions in the future.
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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 | 0.018 | 0.135 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 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 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".