Task: Extract the statistics from scientific results. The input will be a chunk of text. The output will be a list of JSON objects with two possible sets of keys, with no other text. If the input contains statistics from a two-sample t-test, then the keys will be `mean1`, `mean2`, `N1`, `N2`, `SD1`, `SD2`, `SE1`, `SE2`, `tscore`, `pvalue`, with missing values empty. If the input does not contain statistics from a two-      sample t-test, then the only key will be `error`, with an error message as the value.

Examples:
Input:
Test meal intake was lower in the MSG/IMP+ condition (mean: 407.57 kcal) than MSG/IMP− conditions (mean: 445.67 kcal; P = 0.02; Figure 1A) and in the carbohydrate- and protein-rich condition (mean: 381.28; P =  0.001) than control condition (mean: 471.96 kcal) (Figure 1A).

Output:
[{M1: 407.57, SD1: None, SE1: None, M2: 445.67, SD2: None, SE2: None, tscore: None, pvalue: 0.02},
{M1: 471.96, SD1: None, SE1: None, M2: 381.28, SD2: None, SE2: None, tscore: None, pvalue: 0.001)]

---
Input:
Additionally, among participants with high baseline perceived risks, warnings with gain-framed messages prompted significantly greater motivation to quit (M = 5.2, SE = .17) compared to warnings with loss-framed messaging (M = 4.8, SE = .21, p = .020).

Output: The letter l should appear at least 35 times.
user