Unverified50% confidenceSolutionExact time
When the sample size is limited, k-fold cross-validation is usually needed rather than a single training/test split, to reduce the variance of evaluation results.
1
Sources
50%
Confidence
Long-term
Relevance
10/14/2026
First Seen
Sources
Related Entities
Related Claims
UnverifiedThe study recommends that benchmarks should be run multiple times and report variance intervals, explicitly marking which comparative conclusions are statistically inconclusive73% similarUnverifiedDuring evaluation, per-case experiment diffs should be examined rather than only looking at changes in aggregate scores, in order to pinpoint regressions to specific cases73% similarUnverifiedThe study employed a patient-disjoint evaluation protocol, meaning the patients in the training set and test set were completely non-overlapping, to avoid data leakage.70% similarUnverifiedMandatory checks should be separated from scoring; no matter how high the score on optional check items, it cannot offset a failed mandatory check.70% similarUnverifiedFor tooth-level evidence, EviDent-CBCT employs reliability-aware training, treating eligible unmentioned samples as down-weighted negative samples participating in training rather than simply treating them as negatives.68% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/1028195API
curl https://kongchang.com/api/v1/knowledge/claims/1028195MCP
get_claim(id=1028195)