Is it possible with only 2 events in the group of exposed (n=5) and 32 in the group of non-exposed (n=76) to carry out a logistic regression adjusted by the independent variable of interest (dose50) and 4 others (age, sex, seguimiento and iacumulada) ?

Thank you in advance!

Code:
* Example generated by -dataex-. To install: ssc install dataex
clear
input byte(spino dose50) double age long sex double(seguimiento iacumulada)
1 0 58.026009582477755 1  26.61738535249829  55.24709103353867
0 0   52.3668720054757 1  22.77618069815195 48.533880903490754
1 0   54.4394250513347 1 30.036960985626283  90.11088295687885
1 .  58.18754277891855 1  8.936344969199178 20.227241615331963
0 0  51.08829568788501 1 16.334017796030118  33.80698151950719
0 0 42.902121834360024 1  15.97262149212868 27.252566735112936
0 0 49.270362765229294 0  17.08145106091718  36.59958932238193
0 0 54.286105407255306 1 21.385352498288842   45.3388090349076
1 0  52.88158795345654 1   29.0403832991102  60.11225188227242
0 0  56.32032854209446 1  22.94866529774127  34.44216290212184
0 0  46.16016427104723 0   28.5968514715948  78.32443531827516
0 0 25.672826830937716 1  25.77138945927447   58.7378507871321
1 0 54.625598904859686 1  19.23340177960301 32.689938398357285
0 0  51.22518822724162 0 23.025325119780973  47.51266255989049
1 0  61.43189596167009 1  24.76659822039699  50.53524982888433
0 .  57.28952772073922 1  8.164271047227926  33.33607118412046
1 0  64.31759069130733 1 20.227241615331966  46.73511293634497
0 0   47.4798083504449 1 25.568788501026695  39.67967145790554
0 .  57.14989733059548 1  9.149897330595483 27.449691991786448
0 0  70.77618069815195 0 15.222450376454484  39.08555783709787
0 1  54.94318959616701 0 22.094455852156056  44.67898699520876
0 0  61.73305954825462 0 13.259411362080767  19.98631074606434
0 1 15.561943874058864 1  27.34839151266256  68.99383983572895
1 0   64.5284052019165 1 12.224503764544833  36.55578370978782
1 0  59.78370978781656 1  13.54962354551677 30.683093771389462
1 0  46.81177275838467 1 24.435318275154003  69.21013004791239
0 0 45.073237508555785 1 11.200547570157427 22.401095140314855
0 0  59.42505133470226 1 11.039014373716633   33.1088295687885
0 0  45.36618754277892 1  14.39835728952772  31.89869952087611
0 .   55.2498288843258 1  9.363449691991786  28.09034907597536
1 . 63.997262149212865 1  9.793292265571527 23.159479808350444
1 .  54.17659137577002 1  9.338809034907598 28.016427104722794
1 0    70.217659137577 1 11.764544832306639 27.838466803559207
1 . 60.030116358658454 1  8.558521560574949  22.84462696783025
1 0  51.39493497604381 1 24.941820670773442  67.31279945242984
0 0 63.383983572895275 1  14.20123203285421  42.60369609856263
1 0 27.222450376454482 1  22.00684462696783  46.88569472963724
0 0 29.894592744695416 1 22.261464750171115  6.234086242299794
0 0  11.53182751540041 1    22.217659137577 26.573579739904176
0 0  57.78507871321013 1  11.75085557837098  23.16221765913758
0 .   60.3668720054757 1  8.503764544832306 13.973990417522245
0 0  59.80561259411362 1 21.620807665982205  35.18412046543463
0 0  37.42094455852156 1 20.407939767282684 49.111567419575636
1 0  67.96988364134155 1  15.34839151266256 31.852156057494867
1 0 50.672142368240934 1 22.584531143052704  46.17111567419576
0 0  52.51745379876797 1  19.55373032169747 27.236139630390145
0 .  68.90622861054072 1  10.42299794661191 26.628336755646817
0 0 60.747433264887064 1 13.084188911704311 24.974674880219027
0 . 63.408624229979466 1 10.934976043805612 22.182067077344286
0 0  42.60917180013689 1 13.670088980150581 27.444216290212186
0 0  68.23819301848049 1  14.33264887063655  23.81930184804928
0 .  42.91033538672142 0 10.149212867898699  29.48939082819986
0 0  60.48733744010951 1  11.41409993155373 19.299110198494184
0 0  64.30390143737166 1 20.788501026694046  41.92470910335387
1 1  65.81519507186859 1 20.536618754277892 41.738535249828885
0 . 63.096509240246405 1  9.492128678986996 19.392197125256672
0 0  66.33812457221082 1 12.407939767282683  33.28405201916495
1 0  60.58316221765914 1 15.329226557152635 26.064339493497606
0 0  41.67830253251198 1 12.320328542094456  19.97262149212868
1 0  53.99041752224504 1 20.186173853524984  40.91991786447639
0 0   46.0041067761807 1 20.024640657084188  37.04859685147159
1 .  55.36208076659822 1  9.075975359342916  19.01437371663244
0 0  53.23750855578371 0  19.94524298425736 40.197125256673516
1 0 55.865845311430526 1  18.73511293634497  40.64065708418891
1 0 61.158110882956876 1  19.63586584531143  39.52361396303901
1 .  68.23271731690623 1  8.711841204654347 18.978781656399725
1 0   51.8631074606434 1 19.353867214236825  30.43668720054757
0 .  65.00479123887749 1   8.24640657084189 17.125256673511295
0 .  68.47912388774812 1  8.427104722792608 12.873374401095141
0 0  57.77138945927447 1 15.770020533880903 24.971937029431896
0 .  61.29226557152635 1  10.82819986310746 17.776865160848732
1 0  54.59822039698837 1 18.039698836413415  37.06502395619438
1 0  66.10540725530458 1 14.740588637919233    30.072553045859
0 0  29.94113620807666 1  31.23340177960301  74.70499657768652
0 0 32.030116358658454 1  18.05886379192334   36.6735112936345
1 0  69.56605065023956 1 11.994524298425736 24.517453798767967
0 0 63.181382614647504 1  14.76249144421629  32.44626967830253
1 1   62.0041067761807 0  17.44832306639288  35.24435318275154
0 0 63.441478439425055 1 15.526351813826146 31.436002737850785
0 0  62.30527036276523 1 17.319644079397673  18.97056810403833
0 .  64.01916495550992 1  9.900068446269678 19.800136892539356
0 0 48.558521560574945 1 16.999315537303218 26.332648870636554
0 0  50.67488021902806 1 14.116358658453114 14.590006844626968
0 0 63.460643394934976 1   12.7419575633128  8.309377138945926
0 .  40.85694729637235 0  9.201916495550993  28.41615331964408
1 0  63.63586584531143 1 15.039014373716633  31.37303216974675
0 0  38.04791238877481 1 26.855578370978783  38.29705681040383
0 .  54.35728952772074 1  9.388090349075975 19.345653661875428
0 0  63.45242984257358 0 13.535934291581109 30.390143737166326
0 0  54.30527036276523 1  13.88911704312115  28.58590006844627
0 0   56.0766598220397 1 13.963039014373717 28.361396303901437
0 1  64.05201916495551 1 13.464750171115675 27.824777549623544
1 0 59.605749486652975 1 13.505817932922655 26.600958247775495
0 0  55.51266255989049 0 13.242984257357975 27.312799452429843
0 .  66.86652977412732 1  10.03148528405202 20.577686516084874
1 0  58.46406570841889 1 12.876112251882272 28.657084188911703
0 0  56.91991786447639 0 12.632443531827516  20.57494866529774
0 0  47.14305270362765 1 12.295687885010267 21.270362765229294
0 0  42.71321013004791 0 12.462696783025326   25.6974674880219
0 0  36.26830937713895 1 11.603011635865846 25.188227241615333
end
label values sex sex
label def sex 0 "Mujer", modify
label def sex 1 "Hombre", modify

HERE IS WHAT STATA GAVE BACK:

logistic spino i.dose50 c.age i.sex c.seguimiento c.iacumulada

Logistic regression Number of obs = 81
LR chi2(5) = 29.72
Prob > chi2 = 0.0000
Log likelihood = -36.697789 Pseudo R2 = 0.2882

------------------------------------------------------------------------------
spino | Odds Ratio Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
1.dose50 | 2.31999 3.020512 0.65 0.518 .1808273 29.76517
age | 1.131634 .042589 3.29 0.001 1.051166 1.218263
|
sex |
Hombre | 17.67864 21.91597 2.32 0.021 1.556776 200.7574
seguimiento | 1.025891 .1081656 0.24 0.808 .8343609 1.261387
iacumulada | 1.080113 .0417791 1.99 0.046 1.001255 1.165183
_cons | 1.47e-06 5.19e-06 -3.81 0.000 1.47e-09 .0014708
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