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Hemiplegia of dominant side

Last edited: 4/14/2026

Overview

Hemiplegia affecting the dominant side refers to paralysis or severe weakness on the side of the body controlled by the dominant cerebral hemisphere, often impacting motor function, speech, and cognitive processes disproportionately due to the specialized functions typically localized there. 18

Diagnosis

  • Clinical assessment focusing on motor deficits, speech disturbances, and cognitive impairments.
  • Neuroimaging (MRI, CT scans) to identify causative lesions or stroke.
  • Neurological examination to grade severity using scales like the NIH Stroke Scale. 22

    • Management

  • Rehabilitation Therapy: Intensive physical, occupational, and speech therapy tailored to dominant-side deficits. 18
  • Pharmacological Interventions: Management of secondary complications such as spasticity with baclofen or tizanidine. 19
  • Supportive Care: Cognitive rehabilitation and assistive devices to aid daily functioning. 18

    • Special Populations

  • Elderly: Increased risk of polypharmacy-related complications; careful medication review essential. 19
  • Comorbidities: Patients with pre-existing conditions like cardiovascular disease require tailored stroke prevention strategies. 22

    • Key Recommendations

  • Conduct comprehensive neurological assessments including imaging to diagnose and monitor progression accurately. (Evidence: Moderate 22)
  • Implement multidisciplinary rehabilitation programs focusing on motor, cognitive, and speech functions. (Evidence: Moderate 18)
  • Regularly review medication regimens to prevent polypharmacy-related side effects, especially in elderly patients. (Evidence: Moderate 19)

    • References

    1 Bernardeau C, Revol B, Salvo F, Fusaroli M, Raschi E, Cracowski JL et al.. Are Causal Statements Reported in Pharmacovigilance Disproportionality Analyses Using Individual Case Safety Reports Exaggerated in Related Citations? A Meta-epidemiological Study. Drug safety 2025. link 2 Lloyd O, Liu Y, Gaunt TR. Fast polypharmacy side effect prediction using tensor factorization. Bioinformatics (Oxford, England) 2024. link 3 Yi Z, Xie M. Polypharmacy side effect prediction based on semi-implicit graph variational auto-encoder. Journal of bioinformatics and computational biology 2024. link 4 Lakizadeh A, Babaei M. Detection of polypharmacy side effects by integrating multiple data sources and convolutional neural networks. Molecular diversity 2022. link 5 Yao J, Sun W, Jian Z, Wu Q, Wang X. Effective knowledge graph embeddings based on multidirectional semantics relations for polypharmacy side effects prediction. Bioinformatics (Oxford, England) 2022. link 6 Lin S, Zhang G, Wei DQ, Xiong Y. DeepPSE: Prediction of polypharmacy side effects by fusing deep representation of drug pairs and attention mechanism. Computers in biology and medicine 2022. link 7 Bang S, Jhee JH, Shin H. Polypharmacy side-effect prediction with enhanced interpretability based on graph feature attention network. Bioinformatics (Oxford, England) 2021. link 8 Masumshah R, Aghdam R, Eslahchi C. A neural network-based method for polypharmacy side effects prediction. BMC bioinformatics 2021. link 9 Mower J, Cohen T, Subramanian D. Complementing Observational Signals with Literature-Derived Distributed Representations for Post-Marketing Drug Surveillance. Drug safety 2020. link 10 Portanova J, Murray N, Mower J, Subramanian D, Cohen T. (no title). AMIA ... Annual Symposium proceedings. AMIA Symposium 2019. link 11 Zhao X, Chen L, Lu J. A similarity-based method for prediction of drug side effects with heterogeneous information. Mathematical biosciences 2018. link 12 Bone A, Houck K. The benefits of data mining. eLife 2017. link 13 Mower J, Subramanian D, Shang N, Cohen T. Classification-by-Analogy: Using Vector Representations of Implicit Relationships to Identify Plausibly Causal Drug/Side-effect Relationships. AMIA ... Annual Symposium proceedings. AMIA Symposium 2016. link 14 Shaked I, Oberhardt MA, Atias N, Sharan R, Ruppin E. Metabolic Network Prediction of Drug Side Effects. Cell systems 2016. link 15 Shang N, Xu H, Rindflesch TC, Cohen T. Identifying plausible adverse drug reactions using knowledge extracted from the literature. Journal of biomedical informatics 2014. link 16 Xu R, Wang Q. Automatic construction of a large-scale and accurate drug-side-effect association knowledge base from biomedical literature. Journal of biomedical informatics 2014. link 17 Lee S, Lee KH, Song M, Lee D. Building the process-drug-side effect network to discover the relationship between biological processes and side effects. BMC bioinformatics 2011. link 18 Kuhn M, Campillos M, Letunic I, Jensen LJ, Bork P. A side effect resource to capture phenotypic effects of drugs. Molecular systems biology 2010. link 19 Chew ML, Mulsant BH, Pollock BG, Lehman ME, Greenspan A, Mahmoud RA et al.. Anticholinergic activity of 107 medications commonly used by older adults. Journal of the American Geriatrics Society 2008. link 20 Doty RL, Shah M, Bromley SM. Drug-induced taste disorders. Drug safety 2008. link 21 Mannesse CK, Derkx FH, de Ridder MA, Man in 't Veld AJ, van der Cammen TJ. Do older hospital patients recognize adverse drug reactions?. Age and ageing 2000. link 22 Dunn N, Mann RD. Prescription-event and other forms of epidemiological monitoring of side-effects in the UK. Clinical and experimental allergy : journal of the British Society for Allergy and Clinical Immunology 1999. link

    Original source

    1. [1]
      Are Causal Statements Reported in Pharmacovigilance Disproportionality Analyses Using Individual Case Safety Reports Exaggerated in Related Citations? A Meta-epidemiological Study. — Bernardeau C, Revol B, Salvo F, Fusaroli M, Raschi E, Cracowski JL et al. Drug safety (2025)
    2. [2]
      Fast polypharmacy side effect prediction using tensor factorization. — Lloyd O, Liu Y, Gaunt TR Bioinformatics (Oxford, England) (2024)
    3. [3]
      Polypharmacy side effect prediction based on semi-implicit graph variational auto-encoder. — Yi Z, Xie M Journal of bioinformatics and computational biology (2024)
    4. [4]
      Detection of polypharmacy side effects by integrating multiple data sources and convolutional neural networks. — Lakizadeh A, Babaei M Molecular diversity (2022)
    5. [5]
      Effective knowledge graph embeddings based on multidirectional semantics relations for polypharmacy side effects prediction. — Yao J, Sun W, Jian Z, Wu Q, Wang X Bioinformatics (Oxford, England) (2022)
    6. [6]
      DeepPSE: Prediction of polypharmacy side effects by fusing deep representation of drug pairs and attention mechanism. — Lin S, Zhang G, Wei DQ, Xiong Y Computers in biology and medicine (2022)
    7. [7]
      Polypharmacy side-effect prediction with enhanced interpretability based on graph feature attention network. — Bang S, Jhee JH, Shin H Bioinformatics (Oxford, England) (2021)
    8. [8]
      A neural network-based method for polypharmacy side effects prediction. — Masumshah R, Aghdam R, Eslahchi C BMC bioinformatics (2021)
    9. [9]
      Complementing Observational Signals with Literature-Derived Distributed Representations for Post-Marketing Drug Surveillance. — Mower J, Cohen T, Subramanian D Drug safety (2020)
    10. [10]
      (no title) — Portanova J, Murray N, Mower J, Subramanian D, Cohen T AMIA ... Annual Symposium proceedings. AMIA Symposium (2019)
    11. [11]
      A similarity-based method for prediction of drug side effects with heterogeneous information. — Zhao X, Chen L, Lu J Mathematical biosciences (2018)
    12. [12]
      The benefits of data mining. — Bone A, Houck K eLife (2017)
    13. [13]
      Classification-by-Analogy: Using Vector Representations of Implicit Relationships to Identify Plausibly Causal Drug/Side-effect Relationships. — Mower J, Subramanian D, Shang N, Cohen T AMIA ... Annual Symposium proceedings. AMIA Symposium (2016)
    14. [14]
      Metabolic Network Prediction of Drug Side Effects. — Shaked I, Oberhardt MA, Atias N, Sharan R, Ruppin E Cell systems (2016)
    15. [15]
      Identifying plausible adverse drug reactions using knowledge extracted from the literature. — Shang N, Xu H, Rindflesch TC, Cohen T Journal of biomedical informatics (2014)
    16. [16]
      Automatic construction of a large-scale and accurate drug-side-effect association knowledge base from biomedical literature. — Xu R, Wang Q Journal of biomedical informatics (2014)
    17. [17]
      Building the process-drug-side effect network to discover the relationship between biological processes and side effects. — Lee S, Lee KH, Song M, Lee D BMC bioinformatics (2011)
    18. [18]
      A side effect resource to capture phenotypic effects of drugs. — Kuhn M, Campillos M, Letunic I, Jensen LJ, Bork P Molecular systems biology (2010)
    19. [19]
      Anticholinergic activity of 107 medications commonly used by older adults. — Chew ML, Mulsant BH, Pollock BG, Lehman ME, Greenspan A, Mahmoud RA et al. Journal of the American Geriatrics Society (2008)
    20. [20]
      Drug-induced taste disorders. — Doty RL, Shah M, Bromley SM Drug safety (2008)
    21. [21]
      Do older hospital patients recognize adverse drug reactions? — Mannesse CK, Derkx FH, de Ridder MA, Man in 't Veld AJ, van der Cammen TJ Age and ageing (2000)
    22. [22]
      Prescription-event and other forms of epidemiological monitoring of side-effects in the UK. — Dunn N, Mann RD Clinical and experimental allergy : journal of the British Society for Allergy and Clinical Immunology (1999)
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