Imaging correlates of serum enzyme-linked immunoelectrotransfer blot (EITB) positivity in patients with parenchymal neurocysticercosis: results from 521 patients
Bibliographic record
Abstract
BACKGROUND: The presence of perilesional edema among patients with parenchymal neurocysticercosis (pNCC) of various lesion subtypes has not been correlated with results of serum enzyme-linked immunotransfer blot (EITB) for cysticercal antibodies. METHODS: In total, 521 patients with pNCC were classified into solitary cysticercus granuloma (SCG), multiple lesions, at least one of which was an enhancing granuloma (GMNCC), solitary calcified cysticercal lesion (SCC) and multiple calcified cysticercal lesions (CMNCC). The proportion of EITB positivity among each lesion subtype and its association with perilesional edema were determined. RESULTS: There were significantly higher positive EITB results in patients with GMNCC (90/111, 81.1%) compared with other lesion types. Perilesional edema was associated with positive EITB in patients with CMNCC. On univariate analysis, perilesional edema and GMNCC were associated with EITB positivity. On multivariate analysis, only GMNCC (OR 7.5; 95% CI 3.5 to 16.2) was significantly associated with EITB positivity. CONCLUSIONS: In patients with pNCC, the presence of perilesional edema is associated with a higher probability of a positive EITB result in patients with CMNCC, suggesting a synchronicity in the mechanisms associated with formation of perilesional edema and the antibody response in this subtype. In patients with enhancing granulomas, edema is not an independent predictor of a positive EITB, suggesting that the enhancement itself is associated with a strong antibody response.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".