Do we agree on seizure reduction after vagus nerve stimulation? Interrater reliability of retrospective and prospective seizure frequency ratings from the <scp>CONNECTiVOS</scp> database
Bibliographic record
Abstract
OBJECTIVE: Although vagus nerve stimulation (VNS) is a well-established neuromodulation therapy for drug-resistant epilepsy, treatment outcomes remain heterogeneous. One possible source of variability lies in differing interpretations of seizure frequency ratings (SFRs). This study examined interrater reliability (IRR) in SFRs between (1) retrospective clinician-clinician chart reviews and (2) prospective caregiver-clinician reports, and explored sources of disagreement. METHODS: Data were collected from the CONNECTiVOS database. In the retrospective cohort (n = 254), two clinicians independently reviewed medical records and rated seizure frequency across multiple timepoints. In the prospective cohort (n = 214), caregivers and clinicians independently reported SFR in children treated with VNS. IRR was assessed across different measurement thresholds, and potential causes of disagreement were analyzed. RESULTS: Clinician-clinician agreement in retrospective chart reviews was excellent (intraclass correlation coefficient [ICC] > .90, Cohen κ > .80), with 18.8% divergent ratings and 4.8% exceeding the reliable change index. Disagreement was significantly associated with higher mean seizure frequency at baseline (p = .004) and at postoperative timepoints (p < .001). In the prospective caregiver-clinician comparison, agreement for absolute seizure frequency was poor (ICC < .50), with discrepancies in 86.5% of cases, although only 1.8% were statistically significant. When rating pairs diverged, clinicians more often reported lower absolute seizure frequencies (p = .002) and greater relative seizure reductions (p = .023) and were more likely to classify patients as achieving a 90% reduction (p = .043). SIGNIFICANCE: This study highlights interrater variability in both retrospective and prospective SFR assessments, a finding systematically related to baseline seizure frequency. Coarser classifications (e.g., 50% or 90% seizure reduction) may improve agreement but reduce clinical nuance. Future efforts should focus on structured, patient-centered documentation and the development of objective outcome measures in VNS evaluation, particularly for children with high seizure burden.
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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 | 0.039 | 0.130 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| 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".