An odds ratio from a cumulative logistic model evaluates the effect of a predictor on all possible dichotomizations of the response levels formed by splitting the ordered levels into two groups. The model assumes that the effect is the same no matter where the split occurs, which is why you only get a single odds ratio estimate for a continuous predictor. The odds ratios that you want contrast only two distinct response levels instead of all of them split into two groups and are not natural odds ratios obtained from the ordinal model. The odds ratios you want naturally arise from the nominal logistic model using generalized, rather than cumulative, logits.
So, the easiest way to get the odds ratios you want is to change the model to use generalized logits. While it is not impossible to estimate the odds ratios you want from the ordinal model, it would require some work. If you just need the point estimates, you could compute them from the predicted mean values (which are cumulative probabilities) from the OUTPUT statement. But if you also want standard errors for them (and possibly confidence intervals), then you would need to use the NLEstimate macro by writing each odds ratio as a function of the model parameters.
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