Evidence-based guideline: Diagnostic accuracy of CSF 14-3-3 protein in sporadic Creutzfeldt-Jakob disease
Bibliographic record
Abstract
OBJECTIVE: To assess the available evidence for the diagnostic accuracy of CSF testing for protein 14-3-3 in patients with suspected sporadic Creutzfeldt-Jakob disease (sCJD). METHODS: The authors performed a systematic review of the available literature from 1995 to January 1, 2011, to identify articles involving patients who were suspected of having sCJD and who had CSF analysis for protein 14-3-3. Studies were rated according to the American Academy of Neurology classification of evidence scheme for diagnostic studies, and recommendations were linked to the strength of the evidence. A pooled estimate of sensitivity and specificity was obtained for all studies rated Class II or higher. The question asked is "Does CSF 14-3-3 protein accurately identify Creutzfeldt-Jakob disease (CJD) in patients with sCJD?" RESULTS: The analysis was conducted on the basis of samples of 1,849 patients with suspected sCJD from 9 Class II studies. Assays for CSF 14-3-3 protein are probably moderately accurate in diagnosing sCJD: sensitivity 92% (95% confidence interval [CI] 89.8-93.6), specificity 80% (95% CI 77.4-83.0), likelihood ratio of 4.7, and negative likelihood ratio of 0.10. RECOMMENDATION: For patients who have rapidly progressive dementia and are strongly suspected of having sCJD and for whom diagnosis remains uncertain (pretest probability ∼20%-90%), clinicians should order CSF 14-3-3 assays to reduce the uncertainty of the diagnosis (Level B).
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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 | 0.018 | 0.076 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.013 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.010 | 0.003 |
| Research integrity | 0.013 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 0.004 |
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".