R - Interpretation of coefficients and written form of fitted model in lm() linear regression when using poly()

Question

I've tried reading several resources on poly(), I'm not able to see an answer to my question. My question pertains how I might present my fitted linear model in a way that the coefficients are interpretable (if possible).

What does the R function poly really do?

poly function | R Documentation

poly() in lm(): difference between raw vs. orthogonal

I have the following data:

y      x
87.4   16
17.8   7
22.0   8
16.8   10
49.2   13
16.1   5
34.2   11

I fit this as a quadratic model. If I do this simply using I(x^2), the interpretability of the coefficients is straightforward.

easy_model <- lm('y ~ I(x) + I(x^2)', data=renamed_data)
summary(easy_model)

Call: lm(formula = "y ~ I(x) + I(x^2)", data = renamed_data)

Residuals:
     13       4       5       7      10       2       8 
 0.1679  1.8821  4.6964 -8.1786  0.4500 -1.9500  2.9321 

Coefficients:
            Estimate Std. Error t value Pr(>|t|)    
(Intercept)  51.9845    16.8946   3.077  0.03704 *  
I(x)        -10.8732     3.4366  -3.164  0.03405 *  
I(x^2)        0.8173     0.1615   5.062  0.00717 **
--- 
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 5.126 on 4 degrees of freedom 
Multiple R-squared:  0.9744,    Adjusted R-squared:  0.9616  
F-statistic: 76.03 on 2 and 4 DF,  p-value: 0.000657

So I would write the above fitted models as $\hat{y} = 51.9845-10.8732x+0.8173x^2$, and in the context of my data I can explain how these coefficients make sense.

If I do this using poly(), however, I'm not clear on how I might interpret the coefficients, or how I might write my fitted model in the context of my data.

odd_model <- lm('y ~ poly(x, 2)', data=renamed_data)
summary(odd_model)

Call:
lm(formula = "y ~ poly(x, 2)", data = renamed_data)

Residuals:
     13       4       5       7      10       2       8 
 0.1679  1.8821  4.6964 -8.1786  0.4500 -1.9500  2.9321 

Coefficients:
            Estimate Std. Error t value Pr(>|t|)    
(Intercept)   34.786      1.938  17.953 5.66e-05 ***
poly(x, 2)1   57.642      5.126  11.244 0.000356 ***
poly(x, 2)2   25.947      5.126   5.062 0.007173 ** 
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 5.126 on 4 degrees of freedom
Multiple R-squared:  0.9744,    Adjusted R-squared:  0.9616 
F-statistic: 76.03 on 2 and 4 DF,  p-value: 0.000657

Now I understand that this model is the same as the one above, and both yield the same predictions.

> predict.lm(easy_model)
      13        4        5        7       10        2        8 
87.23214 15.91786 17.30357 24.97857 48.75000 18.05000 31.26786 
> predict.lm(odd_model)
      13        4        5        7       10        2        8 
87.23214 15.91786 17.30357 24.97857 48.75000 18.05000 31.26786

My conundrum is that I can't write $\hat{y} = 34.7896+57.642x+25.947x^2$ from the lm output, this gives different values than what the model would actually predict.

Could someone help me understand how I might interpret the coefficients for my model that uses poly(), and how I might present this fitted model in written form?