As a general remark, you only have 151 observations, and you will base your scoring on impressions from graphical displays and descriptive statistics, many statistical significance tests, and a lot of trials (different weighting methods, tranformations of the dependet variables etc.) to produce a model which fits to the data best. All this will probably result in overfitting, the resulting "best" scoring method will perhaps not be well generalizable beyond the n=151 patients in your sample.

There's also the question why tests of significance should be important here?

Any weighting scheme derived from the n=151 sample data will suffer from considerable standard errros of the single parameter estimations included. Therefore, I'd personally follow Jacob Cohen's advice (given in "Things I have learned (so far)" to keep things simple and to use unit weights. This would mean to just start with a simple summation of the 4 independent variables (maybe "other conditions" should be adjusted somehow to a 1-5 range) and check the validity / predictive value of that sum score. As long as there are no serious pre-existing reasons for assuming largely different weights between independent variables, and if independent variables aren't redundant, of course.

Just my 2pence

Karabiner