Should patients use online reviews to pick their doctors and hospitals?
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
Abstract We compare the online reviews of 221 “Questionable” Illinois and Indiana physicians with multiple paid medical malpractice claims and disciplinary sanctions with matched control physicians with clean records. Across five prominent online rating services, we find small, mostly insignificant differences in star ratings and written reviews for Questionable versus control physicians. Only one rating service (Healthgrades) reports on paid medical malpractice claims and disciplinary actions and it misses more than 90% of these actions. We also evaluate the online ratings of 171 Illinois hospitals and find that their ratings are largely uncorrelated with the share of hospital‐affiliated physicians with paid medical malpractice claims and disciplinary sanctions. Online ratings have limited utility in helping patients avoid physicians with troubled medical malpractice and disciplinary records, and steering patients away from hospitals at which more physicians have paid medical malpractice claims and disciplinary sanctions.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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