Datasets:
Preregistration — Calibration & Treatment-Status Benchmark for German Clinical Text (Local LLMs)
Version: 1.0 · Status: PREREGISTERED (to be frozen by git commit before any model is run) Date: 2026-06-13 · Scope: Phase 1 — language models only (Nixi AI product evaluation is out of scope, separate study).
This document commits every analysis choice before inference. The authoritative timestamp is the git commit. Companion frozen artifacts:
annotation_guideline_v1_1.md,grascco_scan_v2.py. Nothing below may be changed after the first model is run; permitted exceptions are logged guideline clarifications (v1.x) that do not retroactively relabel data.
Public-release note (v1, authored arm). This public release covers the authored arm only. The GraSCCo arm and its tooling (
grascco_scan_v2.py,frozen_eval_set.csv) are deferred to a later version and are not included here. The annotation guideline ships asannotation_guideline_v1.2.md, the adjudicated/clarified version of the v1.1 referenced below; the binary safety definition is unchanged across v1.1→v1.2.
1. Background & rationale
In clinical documentation the dangerous error is not a low score — it is a confident wrong answer. A drug that was discussed and declined, planned, stopped, or only historical must not be recorded as administered (a §630a/§630f BGB documentation-liability problem). We measure not just accuracy but calibration: whether a local open-weight LLM's confidence is trustworthy when it classifies the current administration status of a treatment in German clinical text. Everything runs on-prem; no patient data leaves the machine (data-sovereignty thesis demonstrated, not asserted).
2. Research question & hypothesis
Primary question. Can local open-weight LLMs correctly classify whether a treatment mentioned in German clinical text is currently being administered, and is their confidence well-calibrated — especially on the hard cases (negation, refusal, "discussed", paused/stopped, cross-sentence reversal)?
Primary hypothesis (falsifiable). Accuracy and calibration diverge: models look fine on plainly affirmative administration but are overconfident on the epistemically hard classes, and the worst error — confidently asserting current administration when there is none — clusters in those hard cases. A clean opposite result (well-calibrated) is a valid, reportable finding.
3. Primary endpoint
High-confidence false-administration rate = the proportion of truly not-given treatment mentions that a model labels given with confidence ≥ 0.90.
- "given" is defined as CURRENT administration/continuation at the time of documentation (Option A). A drug that is paused, stopped, planned, discussed, refused, withheld, historical, or merely listed is not given, even if administered earlier in the episode.
- Reported two ways: (a) overall; (b) split into active not-given (refused / withheld / paused / stopped / planned — clinically dangerous to call given) vs historical/background (episode-scoping slip). The active number is the headline safety figure.
- Every endpoint figure is reported with a bootstrap 95% CI, resampled at the document level (mentions are clustered within letters; one letter contributes up to 214 mentions).
4. Secondary questions
Does bigger help (Qwen 7→14→32B; Llama 8→70B) · does German-tuning help (SauerkrautLM vs Llama-3.1-8B) · does medical-tuning help (OpenBioLLM, Med42 vs Llama-3.1-8B; BioMistral cross-base) · error direction (do models lean toward "given") · can confidence discriminate right from wrong · is miscalibration fixable with a single temperature parameter · position/letter bias (option permutation) · operational deployability (latency, VRAM) · do models fail on the same items (error correlation) · cross-lingual replication (deferred — see §6).
5. Label scheme (governed by annotation_guideline_v1_1.md)
Annotation = 9 leaves, each with a fixed binary value; the model is graded on binary (primary) and a 6-class mid-level (error taxonomy). Binary uses the Option-A current-administration rule.
| Leaf | Binary | 6-class |
|---|---|---|
| GIVEN · CONTINUED-active | given | GIVEN |
| PLANNED | not_given | PLANNED |
| DISCUSSED | not_given | DISCUSSED |
| REFUSED (+AMA modifier) | not_given | REFUSED |
| WITHHELD/CONTRAINDICATED · PAUSED · STOPPED | not_given | WITHHELD |
| HISTORICAL / background | not_given | HISTORICAL |
| DROP (not a treatment mention) | — | — |
Phenomena tagged for breakdown: negation · refusal · discussion-only · cross-sentence reversal · PRN/bei Bedarf · paused · stopped · contraindication/allergy · historical (Z. n.) · conditional (falls…dann) · frustran.
Section-heading routing, the home-med rule, the "kein = finding" DROP rule, and the validated ambiguous-heading handling ("Aktuelle Medikation" → line-by-line, 48/48 no verb in corpus) are specified in the frozen guideline.
6. Datasets & sample size
| Dataset | Lang | Role | N (target) | Status |
|---|---|---|---|---|
| GraSCCo (synthetic discharge letters) | DE | Primary natural result | ~500–600 labelable items (label all valid mentions from 63 letters) | downloaded, scanned |
| Authored minimal-pair set | DE | Controlled hard-class arm | ~150, weighted to deficient classes | to build |
| i2b2/n2c2 2010 | EN | Cross-lingual replication | — | deferred (access unavailable) |
| BRONCO150 | DE | Real-German validation | — | future phase |
Authored-set targets (natural GraSCCo is GIVEN-scarce and historical-heavy): REFUSED ~40 (0 natural) · GIVEN ~30 · PAUSED ~20 · STOPPED ~20 · PRN ~20 · DISCUSSED ~15 · cross-sentence ~25 · WITHHELD controls ~15–20.
Reporting rule: GraSCCo labeled as-found = the natural (imbalanced) distribution; the authored set = the stratified arm with guaranteed per-class denominators. The two are reported separately, never silently merged.
Scope note: Phase 1 is synthetic German + controlled authored German. Real English (i2b2) and real German (BRONCO) are the planned next phases; this sequencing is stated as a limitation, not hidden.
7. Annotation & label quality
- Primary annotator labels the full set off the cleaned pre-fill (
grascco_candidate_mentions). - Independent human second annotator (a German-speaking clinician, not the primary) labels a 20–30% stratified sample (~120–180 mentions), blind to the primary's labels.
- Report Cohen's κ per leaf and for the binary (targets: binary ≥ 0.80; per-leaf 0.60–0.80 acceptable; < 0.60 → guideline clarification before reliance). Rare leaves will have few items → κ interpreted cautiously.
- Adjudicate disagreements, record resolutions, then freeze the eval set.
- An AI labeling pass, if done, is reported only as an exploratory third signal — never as the κ rater.
8. Models (frozen list)
All open-weight, Ollama-runnable on 48 GB, commercial-safe, clean single-token emitters (no reasoning/CoT models). Verify exact tag, quant, and license on pull; record Ollama version.
| # | Model | Role | Base | Size | Primary quant |
|---|---|---|---|---|---|
| 1 | Llama 3.1 8B Instruct | general baseline (+fp16 anchor) | Llama-3 | 8B | fp16 |
| 2 | SauerkrautLM-8B | German-tuning probe (vs #1) | Llama-3 | 8B | fp16/Q8 |
| 3 | OpenBioLLM-8B | medical-tuning probe (vs #1) | Llama-3 | 8B | fp16/Q8 |
| 4 | Med42-v2 8B | medical-tuning probe #2 | Llama-3 | 8B | fp16/Q8 |
| 5 | Qwen2.5-7B Instruct | size ladder | Qwen2.5 | 7B | fp16/Q8 |
| 6 | Qwen2.5-14B Instruct | workhorse + calibration ref | Qwen2.5 | 14B | Q8 |
| 7 | Qwen2.5-32B Instruct | size step | Qwen2.5 | 32B | Q8 |
| 8 | Gemma 2 27B IT | family diversity | Gemma 2 | 27B | Q8 |
| 9 | BioMistral-7B | cross-base medical | Mistral | 7B | fp16/Q8 |
| 10 | Llama 3.3 70B Instruct | large anchor ("does bigger help?") | Llama-3.3 | 70B | Q4_K_M (caveat) |
Comparison matrix: bigger → Qwen 7/14/32 + Llama 8/70; German-tuning → #2 vs #1; medical-tuning → #3,#4 vs #1, #9 cross-base. Fairness: Q8 (≤32B) / fp16 (≤8B); the 70B is Q4-only on 48 GB — a documented caveat, not a fair-comparison point.
9. Inference protocol (frozen)
- Task A (primary): binary forced choice (given / not-given).
- Task B (secondary): 6-class forced choice (GIVEN/PLANNED/DISCUSSED/REFUSED/WITHHELD/HISTORICAL).
- Forced choice, "answer with one letter only." Aggressive single-letter prompt for verbose medical models so preamble does not pollute first-token logprobs.
- Decoding: temperature 0, fixed seed,
top_logprobs = 20, Ollama ≥ 0.12.11. - Confidence rule: probability of the chosen letter renormalized over the valid option letters only (A–E), not raw vocabulary softmax.
- Passes: one deterministic pass for primary metrics; an N ≥ 20 repeat pass for the determinism layer.
- Pre-run check: confirm on one model that Ollama honors the seed and returns logprobs before pulling the rest.
10. Metrics (frozen; all with document-level bootstrap 95% CIs)
- Calibration: ECE (equal-width + adaptive), MCE, Brier, log loss, calibration slope+intercept, temperature T + post-scaling ECE, class-wise ECE.
- Discrimination / abstention: risk–coverage, accuracy@{.7,.8,.9,.95}, AUROC, AUPRC (false-given), AURC/E-AURC.
- Classification: accuracy, balanced accuracy, macro-F1, per-class F1, MCC, Cohen's κ vs gold, full confusion matrix, directional error rates (false-given vs false-not-given).
- Operational: latency p50/p95, tokens/sec, VRAM peak, load time, clean-emission/parse-fail rate.
- Robustness: flip rate, modal agreement, position-bias (option permutation), prompt-paraphrase variance.
Primary analysis: compute the high-confidence false-administration rate (§3) per model, overall and split active-vs-historical, with document-level bootstrap CIs. The reliability diagram and the headline rate lead the report; note explicitly if the best-F1 model is not the best-calibrated.
11. Honesty & validity (to be stated in the write-up)
- Phase 1 = synthetic German + authored German; real English (i2b2) and real German (BRONCO) are named next phases, not hidden weaknesses.
- German labels are our judgments until the κ pass; κ is reported.
- Forced choice removes the option to abstain; the risk–coverage view (and a future verbalized-confidence arm) partially address this.
- Report unweighted numbers even if a safety-weighted score is also shown.
- Pin all versions (Ollama, model tags, quant, seed) for reproducibility.
12. Deliverables checklist
- Preregistered protocol (this file — commit to freeze)
- Frozen annotation guideline (
annotation_guideline_v1_1.md) - Extraction/scan tooling (
grascco_scan_v2.py), cleaned pre-fill CSV - Frozen eval sets: GraSCCo-labeled + authored minimal pairs
- Second-annotator κ reported
- Inference + scoring code (versions pinned)
- Metrics tables with CIs + figures (reliability diagram, "where it breaks", risk–coverage)
- The post (models-only), with limitations and links
13. What is frozen vs. changeable
Frozen now (no change after first model run): research question, primary endpoint + Option-A "given" definition, secondary questions, 9-leaf scheme + binary/6-class mappings, phenomena list, datasets + N targets + reporting rule, model list + comparison matrix, inference protocol, confidence rule, metric list, bootstrap method.
Permitted: guideline clarifications logged as v1.x (no retroactive relabeling); recording exact model tags/quant/Ollama version on pull; extending the determinism N if flip rate is high.