Table 1 Demographics and discharge status of the 337 hospital admissions in the database.

Variables	Total (n = 337)	Female (n = 103)	Male (n = 234)	P*
Age_cut, n (%)				0.673
(0,18]	2 (1)	0 (0)	2 (1)
(18,30]	8 (2)	2 (2)	6 (3)
(30,40]	10 (3)	2 (2)	8 (3)
(40,50]	23 (7)	4 (4)	19 (8)
(50,60]	41 (12)	11 (11)	30 (13)
(60,70]	87 (26)	26 (25)	61 (26)
(70,80]	95 (28)	30 (29)	65 (28)
(80,90]	52 (15)	21 (20)	31 (13)
(90,150]	19 (6)	7 (7)	12 (5)
DaysHospitalStay, Median (Q1,Q3)	22 (12, 36)	20 (12, 30)	22 (12.75, 38)	0.224
StatusOnDischarge, n (%)				0.427
Cured	185 (56)	55 (53)	130 (57)
Not cured	121 (36)	37 (36)	84 (37)
Unknown	26 (8)	11 (11)	15 (7)

^*The P value represents the probability of observing the current or more extreme differences in continuous or categorical variables between female and male patients, assuming there is no true difference between the groups (i.e., the null hypothesis). A p-value less than the predetermined significance level, often 0.05, suggests that the differences observed are statistically significant, indicating a potential biological or clinical relevance in gender comparisons. The t-test is applicable when data are normally distributed and assumes equal variances between groups, calculating the t-statistic to determine the p-value from the t-distribution. In cases where normality is questionable, the non-parametric Mann-Whitney U test is used, which ranks data across both groups and calculates the p-value based on the sum of ranks. For categorical data, the chi-square test of independence is often used to compare the distribution of categorical variables between genders, with the p-value derived from the chi-square distribution. Fisher’s Exact Test is an alternative when sample sizes are small or expected frequencies are too low, providing an exact p-value based on the hypergeometric distribution.

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