Bibliographic record
Abstract
The article by Chung and colleagues on the discordance between community- and hospital-based ultrasound is striking because of the error rate data. Community based ultrasounds, typically performed by a technician, were discordant for significant genito-urinary abnormalities compared with subsequent hospital based ultrasounds or CT scans in about half of cases. False-positive results were the most prevalent error. The authors conclude that the performance of ultrasound without direct involvement of a radiologist was responsible for the relatively poorer quality of community-based studies. The article is timely in the context of 2 recent widely publicized episodes of medical error in Newfoundland. Faulty estrogen receptor tests disqualified scores of women with breast cancer in Newfoundland and Labrador from receiving an anti-estrogen. Of 763 patients who tested negative, 317 turned out to be receptor positive, a false negative rate of 42%. Last month, 2 radiologists in that province were suspended within weeks of one another. The first, Dr. Fred Kasirye, was responsible for misreading hundreds of x-rays. His resignation was followed by a massive undertaking to re-read all of the 2000 studies he had interpreted. A second radiologist was suspended recently, and subsequently reinstated when it was determined that fewer than 10% of the reports were questionable. More recently, the CEO of Newfoundland's Health Authority, George Tilley, resigned over the handling of these issues. These events increase costs dramatically, and most important, they undermine trust in physicians and contribute to patient anxiety. These errors have led to inappropriate management in some cases. The Chung paper demonstrates that questionable interpretation of imaging studies is not confined to Newfoundland. All physicians are vulnerable to medical error. In most cases, serious mistakes occur when several safeguards have gone awry. Management theory holds that minor process flaws may point to underlying hazards that are catastrophic under different circumstances. The airline industry recognizes this. When near misses occur, the cause is sought even though both planes landed safely. As an analogy, the fact that the CT scan is not available in the operating room today didn't result in a problem, because the dictated note and ultrasound both identify the correct side of the tumour. Tomorrow, those notes also might not be available, or might contain an error, and the wrong kidney might be removed. Or the wrong drug administered. In an imperfect health care system operated and managed by human beings, errors occur. The implications are pretty clear. Know your pathologist. Review radiologic and pathologic findings yourself. Be skeptical. Analyze minor glitches to identify systemic problems, and correct them. Primum non nocere.
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 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 | 0.099 | 0.428 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.010 | 0.007 |
| Science and technology studies | 0.011 | 0.056 |
| Scholarly communication | 0.022 | 0.037 |
| Open science | 0.007 | 0.019 |
| Research integrity | 0.018 | 0.025 |
| Insufficient payload (model declined to judge) | 0.020 | 0.015 |
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".