You are now a sqlite data analyst, and you are given a database schema as follows:

    【Schema】
    【DB_ID】 thrombosis_prediction
【Schema】
# Table: Laboratory
[
(ID:INTEGER, Primary Key, Examples: [27654, 30609, 43003]),
(Date:DATE, Primary Key, Examples: [1991-09-11]),
(GOT:INTEGER, Examples: [34, 29, 26]),
(GPT:INTEGER, Examples: [36, 31, 22]),
(LDH:INTEGER, Examples: [567, 579, 684]),
(ALP:INTEGER, Examples: [166, 154, 138]),
(TP:REAL, Examples: [4.5, 5.1, 5.5]),
(ALB:REAL, Examples: [3.3, 3.4, 3.6]),
(UA:REAL, Examples: [3.8, 4.2, 4.9]),
(UN:INTEGER, Examples: [29, 36, 34]),
(CRE:REAL, Examples: [0.8, 0.9, 0.7]),
(T-BIL:REAL, Examples: [0.3, 0.2, 0.4]),
(T-CHO:INTEGER, Examples: [165, 134, 140]),
(TG:INTEGER, Examples: [185, 205, 254]),
(CPK:INTEGER, Examples: [9, 10, 11]),
(GLU:INTEGER, Examples: [88, 134, 161]),
(WBC:REAL, Examples: [5.0, 10.4, 10.5]),
(RBC:REAL, Examples: [2.6, 2.9, 3.4]),
(HGB:REAL, Examples: [6.4, 6.7, 7.9]),
(HCT:REAL, Examples: [20.3, 21.6, 24.7]),
(PLT:INTEGER, Examples: [227, 242, 233]),
(PT:REAL, Examples: [11.3, 12.0, 11.8]),
(APTT:INTEGER, Examples: [108, 84, 76]),
(FG:REAL, Examples: [27.0, 38.6, 27.6]),
(PIC:INTEGER, Examples: [320, 513, 284]),
(TAT:INTEGER, Examples: [77, 108, 115]),
(TAT2:INTEGER, Examples: [113, 88, 104]),
(U-PRO:TEXT, Examples: [-, 0, TR]),
(IGG:INTEGER, Examples: [339, 771, 430]),
(IGA:INTEGER, Examples: [145, 188, 118]),
(IGM:INTEGER, Examples: [46, 132, 56]),
(CRP:TEXT, Examples: [0.6, 2.7, 1.2]),
(RA:TEXT, Examples: [-, +, +-]),
(RF:TEXT, Examples: [<20.5, <20.8, <40]),
(C3:INTEGER, Examples: [30, 82, 103]),
(C4:INTEGER, Examples: [14, 26, 35]),
(RNP:TEXT, Examples: [0, 1, 256]),
(SM:TEXT, Examples: [0, 1, negative]),
(SC170:TEXT, Examples: [16, 1, 0]),
(SSA:TEXT, Examples: [0, negative, 16]),
(SSB:TEXT, Examples: [0, negative, 1]),
(CENTROMEA:TEXT, Examples: [0, negative]),
(DNA:TEXT, Examples: [41.9, 18, 4.3]),
(DNA-II:INTEGER)
]
# Table: Patient
[
(ID:INTEGER, Primary Key, Examples: [2110, 11408, 12052]),
(SEX:TEXT, Examples: [F, M]),
(Birthday:DATE, Examples: [1934-02-13]),
(Description:DATE, Examples: [1994-02-14]),
(First Date:DATE, Examples: [1993-02-10]),
(Admission:TEXT, Examples: [+, -, +(]),
(Diagnosis:TEXT, Examples: [RA susp., PSS, SLE])
]
# Table: Examination
[
(ID:INTEGER, Examples: [14872, 48473, 102490]),
(Examination Date:DATE, Examples: [1997-05-27]),
(aCL IgG:REAL, Examples: [1.3, 4.3, 2.3]),
(aCL IgM:REAL, Examples: [1.6, 4.6, 2.5]),
(ANA:INTEGER, Examples: [256, 0, 16]),
(ANA Pattern:TEXT, Examples: [P, P,S, S]),
(aCL IgA:INTEGER, Examples: [0, 3, 4]),
(Diagnosis:TEXT, Examples: [MCTD, AMI, SLE, PSS]),
(KCT:TEXT, Examples: [-, +]),
(RVVT:TEXT, Examples: [-, +]),
(LAC:TEXT, Examples: [-, +]),
(Symptoms:TEXT, Examples: [AMI, CNS lupus, Apo]),
(Thrombosis:INTEGER, Examples: [1, 0, 2])
]
【Foreign keys】
Laboratory.ID=Patient.ID
Examination.ID=Patient.ID

    【Question】
    For patients with abnormal platelet level, state the number of patients with lower than normal range. How is it compare to the number of patients with higher than normal range? abnormal platelet level refers to PLT <= 100 or PLT >= 400; platelet level lower than normal range refers to PLT < 100; calculation = SUBTRACT(SUM(PLT < 100), SUM(PLT > 400)); platelet level higher than normal range refers to PLT > 400;

    【Evidence】
    abnormal platelet level refers to PLT <= 100 or PLT >= 400; platelet level lower than normal range refers to PLT < 100; calculation = SUBTRACT(SUM(PLT < 100), SUM(PLT > 400)); platelet level higher than normal range refers to PLT > 400;

    Please read and understand the database schema carefully, and generate an executable SQL based on the user's question and evidence. 

SQL: SELECT SUM(CASE WHEN T2.PLT <= 100 THEN 1 ELSE 0 END) - SUM(CASE WHEN T2.PLT >= 400 THEN 1 ELSE 0 END) FROM Patient AS T1 INNER JOIN Laboratory AS T2 ON T1.ID = T2.ID