references/reader_elicitation_design.md | the design has a human-rater or expert-elicitation arm — rubric axes, calibration probes, operational rigor, human-as-operator, and the six ceiling decisions | ~2,400 tokens, none of which applies to a design with no reader arm |
references/dag_adjustment.md | confounding control needs an explicit adjustment set | — |
references/target_trial_emulation.md | the design emulates a target trial | — |
references/venue_accept_recipe.md | it is a clinical DL / AI-validation study and the question is which venue tier the achievable design can be accepted at, and the one design move that reaches the tier above (the design→acceptance-tier ladder + the five acceptance levers, reverse-engineered from accepted OA papers; the bridge into /find-journal) | ~1,800 tokens; skip for a design with no publication-tier decision |
references/combine_models_ablation_design.md | the model is built by combining / adapting / fine-tuning existing models (nnU-Net, TotalSegmentator, SAM/MedSAM, a pretrained backbone) — how to design the comparator as an ablation that proves the combination earns its complexity (beat the un-adapted base + the best single component + direct-train), reverse-engineered from accepted OA papers | ~1,600 tokens; skip if the model is trained de novo with no reused component |
references/multi_model_comparison_design.md | the study's contribution is comparing several models / architectures head-to-head (CNN vs Transformer vs foundation backbone; N segmentation networks) — how to make the comparison fair: one frozen split + one preprocessing through every model, a strong self-configuring baseline (nnU-Net) not a hobbled one, matched training/HPO/compute budget (the #1 threat — "new≠better, just tuned harder") or disclosed, variance-over-single-run, a pre-specified primary metric + a paired delta test, honest ranking. Reverse-engineered from accepted OA papers | ~1,700 tokens; skip for a single-model study (use combine_models_ablation_design.md for an ablation of one model, /design-ai-benchmarking for AI-vs-human) |
references/segmentation_failure_characterization_design.md | the claim is that a segmentation model is clinically usable, not that it scores well — a pre-specified failure taxonomy (boundary drift / missed / hallucinated / catastrophic), an acceptability endpoint with a stated scale, named judges and an adjudication rule, the tail (per-case distribution, worst percentile, catastrophic count) beside the mean, edit effort paired against manual-from-scratch and disaggregated per structure and site, and failures stratified by what predicts them. Reverse-engineered from accepted OA papers | ~1,700 tokens; skip when the endpoint is benchmark accuracy with no usability claim (metric choice → /model-evaluation; abstention / risk–coverage → /uncertainty-imaging) |
