Unverified50% confidenceFactExact time
Among the set of evaluated models, medical-adapted models have higher omission rates than general-purpose models.
1
Sources
50%
Confidence
Long-term
Relevance
10/27/2026
First Seen
Sources
Related Entities
Related Claims
UnverifiedOn the strongest models, the differences between experimental groups fall within confidence intervals, and researchers characterized the results as directional patterns and soundness properties rather than proven performance lower bounds74% similarUnverifiedThe average evaluation metrics of a new LLM may be better than those of an older model, but regressions can occur in niche critical scenarios.73% similarUnverifiedMedical fine-tuning changes the model's style of expressing itself, not its metacognition; after fine-tuning the model is less overconfident, but the internal gauge for distinguishing what it knows from what it does not has not become more accurate.73% similarUnverifiedWhen the proportion of diseased samples is low, recall and F1 score reflect the model's actual detection capability better than accuracy.73% similarUnverifiedWhen a predictive model performs excellently but clinicians cannot take practical action based on it, its impact on outcomes may be minimal.70% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/1102998API
curl https://kongchang.com/api/v1/knowledge/claims/1102998MCP
get_claim(id=1102998)