Measuring long-term psychiatric outcomes in post-acute autoimmune encephalitis
Bibliographic record
Abstract
PURPOSE: To compare the performance of different measures of long-term psychiatric outcomes in patients with post-acute autoimmune encephalitis (AE) who may require comprehensive psychiatric evaluation. METHODS: The sensitivity of three self-reported measures of mood and anxiety symptoms (Patient Health Questionnaire [PHQ-9]; Profiles of Mood States-2 [POMS-2]; Generalized Anxiety Disorder 7-item [GAD7]) was compared with a structured clinician-administered tool (Mini Neuropsychiatric Inventory 7.0.2 [MINI]). New cutoff scores that optimized accuracy were then identified by Youden Index Method. RESULTS: Thirty-five patients with post-acute AE completed testing a median of 3 years after symptomatic onset (range = 1-22 years). The median PHQ9 score was 5 (range = 0-18), median POMS2 Total Mood Disturbance T-Score was 52 (range = 37-93), and median GAD7 score was 3 (range = 0-17). Twenty-five patients (71 %) met criteria for a psychiatric diagnosis on the MINI. When compared with the MINI, the sensitivity and specificity of the self-reported psychiatric symptom tools using standard cutoffs were 25 % and 80 % for the PHQ9, 50 % and 87 % for the POMS-2, 23 % and 91 % for the GAD7. Accuracy was improved when cutoffs of ≥5 for the PHQ9, ≥50 for the POMS2, and ≥ 3 for the GAD7 were used, at the cost of lower specificity. CONCLUSIONS: Patients with post-acute AE with psychiatric sequalae may be underrecognized if self-reported measures of psychiatric symptoms are used instead of clinician-administered structured interviews. If self-reported measures are used in AE, consideration should be given into using tools with higher validity in this patient population, such as the POMS-2.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".