Proposal of new clinical diagnostic criteria for fatal familial insomnia
Bibliographic record
Abstract
Abstract Background Early and accurate diagnosis of fatal familial insomnia (FFI) is critical for effective screening; however, this rare disease remains difficult to recognize. This work aimed to propose a new diagnostic criterion for fatal familial insomnia (FFI) with optimal sensitivity, specificity, and likelihood ratio. Methods An international group of experts was established and 128 genetically confirmed FFI cases and 281 non‐FFI prion disease controls are enrolled in the validation process. The new criteria were proposed based on the following steps with 2‐round expert consultation: 1) Validation of the 2018 FFI criteria. 2) Diagnostic item selection according to statistical analysis and expert consensus. 3) Validation of the new criteria. Results The 2018 criteria for possible FFI had a sensitivity of 90.6%, specificity of 83.3%, the positive likelihood ratio (PLR) of 5.43, and negative likelihood ratio (NLR) of 0.11; the probable FFI criteria had a sensitivity of 83.6%, specificity of 92.9%, the PLR of 11.77, and NLR of 0.18. The new criteria included more specific and/or common clinical features, 2 exclusion items, and summarized a precise and flexible diagnostic hierarchy. The new criteria for possible FFI had a sensitivity of 92.2%, specificity of 96.1%, a PLR of 23.64, and NLR of 0.08, while the probable FFI criteria had a sensitivity of 90.6%, specificity of 98.2%, the PLR of 50.33, and NLR of 0.095. Conclusion A new clinical diagnostic criteria was proposed for FFI, which will be practical at the clinic for early recognition of FFI and differentiation from other prion diseases then help further referral and genetic test.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".