cbct-clinical-reasoner / results.json
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{
"provenance": {
"commit": "fb1be803c04a109f15aba152f6660a7b8463be74",
"branch": "feat/toothfairy4-pipeline",
"config": {
"name": "toothfairy4-prototype-v1",
"seed": 2026,
"folds": 5,
"preprocess": {
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0.5,
0.5,
0.5
],
"shape_zyx": [
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],
"clip_percentiles": [
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],
"orientation": "LPS",
"dtype": "float16"
},
"prototypes": {
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"embedder": "tfidf",
"linkage_threshold": 0.62,
"max_sentence_words": 60,
"assign_threshold": 0.45,
"tooth_aware": true
},
"encoder": {
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"timm_model": "convnext_tiny",
"width": 32,
"dropout": 0.2,
"epochs": 40,
"batch_size": 4,
"accumulate": 2,
"learning_rate": 0.0002,
"weight_decay": 0.02,
"warmup_ratio": 0.1,
"label_smoothing": 0.02,
"focal_gamma_negative": 2.0,
"focal_gamma_positive": 0.0,
"ema_decay": 0.999,
"amp": true,
"num_workers": 8,
"min_head_support": 0,
"seed": 2026
},
"llm": {
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"enabled": false,
"lora_rank": 32,
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"epochs": 3,
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"max_length": 1536,
"max_new_tokens": 512
},
"decode": {
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"clinical_weight": 0.8,
"captioning_weight": 0.2,
"calibration_rounds": 6,
"refine_top": 250,
"min_sentences": 4,
"max_sentences": 16,
"mbr_candidates": 24
}
}
},
"dataset": {
"cases": 622,
"prototypes": 989,
"assign_threshold": 0.45,
"tooth_aware": true
},
"training": {
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"fold_val_map": [
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0.06671829082485603,
0.06935018761915886,
0.08048351787180753,
0.06401017932076238
],
"mean_val_map": 0.0737782531351602
},
"calibration": {
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"clinical": 0.3862701303619008,
"captioning": 0.22285264809233002,
"logical_precision": 0.4928832064684155,
"logical_recall": 0.3175767155686247,
"bleu_4": 0.14492126109494552,
"meteor": 0.3007840350897145,
"mean_sentences": 12.257234726688102
},
"baseline_fixed_threshold": {
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},
"rounds": 6
},
"evaluation": {
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"clinical": 0.38607203617714003,
"captioning": 0.22783800067504528,
"logical_precision": 0.49096604984868647,
"logical_recall": 0.3181086871959718,
"bleu_4": 0.14926612750396082,
"meteor": 0.3064098738461298,
"num_cases": 622,
"clinical_source": "lexical-surrogate"
},
"evaluation_radfact_lite": null,
"ablation": {
"corpus prior (no imaging)": {
"final": 0.35751793649563435,
"clinical": 0.39374008539376365,
"captioning": 0.21262934090311725,
"logical_precision": 0.5385852090032154,
"logical_recall": 0.31029144605574627,
"bleu_4": 0.11701313497207318,
"meteor": 0.30824554683416133,
"num_cases": 622
},
"linear, 122 features": {
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"clinical": 0.38607203617714003,
"captioning": 0.22783800067504528,
"logical_precision": 0.49096604984868647,
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"bleu_4": 0.14926612750396082,
"meteor": 0.3064098738461298,
"num_cases": 622
},
"fine-tuned encoder (29M params)": {
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"clinical": 0.3752387592094134,
"captioning": 0.20070984127472175,
"logical_precision": 0.5481078407123423,
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"bleu_4": 0.13306690758262765,
"meteor": 0.26835277496681587,
"num_cases": 622
}
},
"caveats": [
"Clinical scores are the repository's offline RadFact surrogate unless 'evaluation_radfact_lite' is populated. The surrogate ranks decoder variants; it is not the challenge metric.",
"BLEU-4 and METEOR are exact reimplementations of the grader's local implementations and match NLTK to machine precision.",
"Scores are out-of-fold on the public training release, not the hidden 50-case test set, and the folds are stratified rather than leave-one-centre-out, so they measure in-domain performance."
]
}