The short answer is that these results provide weak evidence for the occurrence of discoloration as affected by the other variables.
d < data.frame(
Gender = as.factor(c(
"M", "M", "M", "F", "F", "M",
"M", "F", "M", "F", "M", "M", "F", "F", "F", "M", "F", "F",
"F", "M", "F", "M", "F", "F", "F", "M", "M", "F", "F",
"F", "M", "M", "F", "F", "F", "F", "F", "F", "F", "M"
)),
Age = c(
62, 45, 71, 70, 57, 76,
69, 69, 53, 55, 69, 74, 81, 63, 70, 68, 67, 74,
69, 66, 70, 46, 58, 69, 53, 50, 62, 60, 60,
65, 47, 39, 81, 81, 54, 63, 76, 71, 66, 61
),
Dose.MG = c(
780800, 1777200, 565200,
213200, 2446800, 389200, 608800, 640000, 308400,
754800, 281000, 339400, 264400, 446400, 736000, 315300,
1319100, 942400, 1057600, 1512400, 1126400, 438000,
366400, 528400, 296400, 1032000, 2452000, 1304500,
1372400, 851200, 254000, 404800, 2236300, 1207100,
1557100, 2931200, 368600, 2646600, 2117300,
668000
),
Discooration = c(
0, 0, 0, 0, 1, 0, 0, 1,
0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0,
0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 1, 0, 0,
0, 1, 1, 0
),
Time.in.months = c(
64, 146, 47, 33, 25, 32,
50, 173, 26, 64, 25, 52, 43, 157, 61, 26, 113,
78, 87, 123, 93, 43, 31, 44, 25, 85, 202, 113,
113, 70, 21, 34, 30, 184, 123, 227, 29, 238,
162, 55
)
)
full_fit < glm(Discooration ~.,d,family=binomial)
summary(full_fit)
#>
#> Call:
#> glm(formula = Discooration ~ ., family = binomial, data = d)
#>
#> Coefficients:
#> Estimate Std. Error z value Pr(>z)
#> (Intercept) 4.214e+00 3.707e+00 1.137 0.2557
#> GenderM 7.634e01 1.102e+00 0.693 0.4883
#> Age 1.487e02 5.151e02 0.289 0.7728
#> Dose.MG 9.029e07 6.926e07 1.304 0.1924
#> Time.in.months 1.389e02 8.434e03 1.647 0.0995 .
#> 
#> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#>
#> (Dispersion parameter for binomial family taken to be 1)
#>
#> Null deviance: 44.987 on 39 degrees of freedom
#> Residual deviance: 32.259 on 35 degrees of freedom
#> AIC: 42.259
#>
#> Number of Fisher Scoring iterations: 5
^{Created on 20231119 with reprex v2.0.2}
The logistic regression model summary can be interpreted as follows:
Interpretation of Coefficients
The coefficients (Estimate) represent the change in the logodds of the outcome for a oneunit increase in the predictor variable, assuming all other variables are held constant.

GenderM: The coefficient of 0.7634 suggests that being male (GenderM) decreases the logodds of discoloration by 0.7634 units compared to the reference category (presumably female), assuming all other variables are held constant.

Age: The coefficient of 0.01487 suggests that for each additional year of age, the logodds of discoloration increase by 0.01487 units, assuming all other variables are held constant.

Dose.MG: The coefficient of 0.0000009029 suggests that for each additional milligram of the dose, the logodds of discoloration increase by 0.0000009029 units, assuming all other variables are held constant.

Time.in.months: The coefficient of 0.01389 suggests that for each additional month, the logodds of discoloration increase by 0.01389 units, assuming all other variables are held constant.
Significance of Coefficients
The Pr(>z) column represents the pvalues associated with the coefficients. If the pvalue is less than a certain significance level (commonly 0.05), this indicates that the predictor variable has a statistically significant relationship with the response variable in the model. The term "significant" should be understood to indicate a degree of confidence that the test statistics are not simply a result of random variation.

GenderM: The pvalue of 0.4883 suggests that gender is not statistically significant in predicting discoloration at the 0.05 significance level.

Age: The pvalue of 0.7728 suggests that age is not statistically significant in predicting discoloration at the 0.05 significance level.

Dose.MG: The pvalue of 0.1924 suggests that the dose is not statistically significant in predicting discoloration at the 0.05 significance level.

Time.in.months: The pvalue of 0.0995 suggests that time in months is not statistically significant in predicting discoloration at the 0.05 significance level, but it is significant at the 0.1 level.
Model Fit Statistics

Null deviance: This is a measure of how well the response variable is predicted by a model that includes only the intercept (no predictors). The null deviance in your model is 44.987 on 39 degrees of freedom.

Residual deviance: This is a measure of how well the response variable is predicted by the model. The residual deviance in your model is 32.259 on 35 degrees of freedom. The decrease in deviance when going from the null model to your model suggests that your model is an improvement over the null model.

AIC: The Akaike Information Criterion (AIC) is a measure of the relative quality of statistical models. Lower values are better. Your model has an AIC of 42.259. Without another model to compare it to, it's hard to interpret this value in isolation.