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Record W7034091418

A Study of the Effect of Treatment On the Clinical Profile, Pain, and Disability in Migraine Patients Seen in a Tertiary Hospital

2024· article· en· W7034091418 on OpenAlexaboutno aff

Bibliographic record

VenueDOAJ (DOAJ: Directory of Open Access Journals) · 2024
Typearticle
Languageen
FieldArts and Humanities
TopicTechnology, Environment, Urban Planning
Canadian institutionsnot available
Fundersnot available
KeywordsMigraineChristian ministryUniversity hospitalPharmacyClinical pharmacyResearch centre
DOInot available

Abstract

fetched live from OpenAlex

Geetha Kandasamy,1 Dalia Almaghaslah,1 Mona Almanasef,1 Tahani Musleh Almeleebia,1 Khalid Orayj,1 Ayesha Siddiqua,1 Eman Shorog,1 Asma M Alshahrani,1 Kousalya Prabahar,2 Vinoth Prabhu Veeramani,2 Palanisamy Amirthalingam,2 Saleh Alqifari,2 Naif Alshahrani,3 Aram Hamad AlSaedi,4 Alhanouf A Alsaab,5 Fatimah Aljohani,6 M Yasmin Begum,7 Akhtar Atiya8 1Department of Clinical Pharmacy, College of Pharmacy, King Khalid University, Abha, Kingdom of Saudi Arabia; 2Department of Pharmacy Practice, Faculty of Pharmacy, University of Tabuk, Tabuk, Kingdom of Saudi Arabia; 3Department of Pharmacy, Ad Diriyah Hospital, Ministry of Health (MOH), Riyadh, 13717, Kingdom of Saudi Arabia; 4College of Medicine, Taibah University, Al Madinah, Al Munawwarah, Saudi Arabia; 5Pharmacist at Abha International Private Hospital, Abha, Saudi Arabia; 6Pharmacist at Prince Sultan Armed Forces Hospital, Almadenah, Almonwarah, Saudi Arabia; 7Department of Pharmaceutics, College of Pharmacy, King Khalid University, Abha, Kingdom of Saudi Arabia; 8Department of Pharmacognosy, College of Pharmacy, King Khalid University, Abha, Kingdom of Saudi ArabiaCorrespondence: Geetha Kandasamy, Department of Clinical Pharmacy, College of Pharmacy, King Khalid University, Abha, Kingdom of Saudi Arabia, Email glakshmi@kku.edu.saBackground: Migraine is a disabling disease that poses a significant societal burden. Migraine is a major cause of disability. Migraine is the eighth leading disease-causing disability in the population.Objective: To study the clinical profile and measure the pain and migraine-related disability of patients with all types of migraine using the McGill pain assessment scale and Migraine Disability Assessment (MIDAS) before and after 3 months of effect on the medication.Methods: A Prospective-Cross sectional study was carried out in a multispecialty hospital with male and female patients between 18 and 65 years. The data were collected from the patients directly through the questionnaire of McGill pain assessment scale-short form (SF) and MIDAS, which was provided before and after the medication. Results: There were 165 subjects of which 52 were men and 113 were women. The mean age of all the subjects was 43 years. About 26.06% of the subjects had a family history of headaches. The scores of McGill pain and MIDAS assessment before and after medication were as follows: 0– 15 were 30.90% and 73.33%, Score 16– 30 were 54.54% and 18.18%, the score of 31– 45 were 14.54% and 7.87% of the subjects. MIDAS grade I was 17.57% and 50.90%, Grade II 33.93% and 21.81%, Grade III 30.30% and 15.75% Grade IV 18.18% and 11.51% of the subjects.Discussion: The calculated “t” value between the before and after medication values of McGill and MIDAS by paired ‘t-test was 13.85 and 17.49 respectively. As the calculated “t” value is more than the table value, the alternate hypothesis is accepted.Conclusion: This study confirms that there is a significant difference in disability levels before and after acute and preventative treatments when measured over 3 months. In addition, the preponderance of females was high, and the functional disability that affects work and social activity associated with migraine is moderate to severe.Keywords: migraine, pain, disability, loss of productivity, MIDAS

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.144
GPT teacher head0.478
Teacher spread0.334 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations0
Published2024
Admission routes1
Has abstractyes

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