The Clinical Spectrum of Multiple Sclerosis in a Tertiary Care Hospital
Bibliographic record
Abstract
Background: Multiple sclerosis (MS) is a chronic autoimmune, inflammatory, demyelinating disease of the central nervous system (CNS) mediated by an inappropriate immune response within the body against the insulating myelin sheath. Methods: Between January 2016 and March 2019, 20 diagnosed cases of MS were recruited for the study. Patient history was collected using a pre-designed standardized clinical proforma. Results: The outcomes of the present study reveal a resemblance of MS patterns in Andhra Pradesh with India and with the West. MS is more common in women. The second and third decades are the most common. The incidence of the disease decreased with age. In comparison to relapsing-remitting MS (RRMS), primary-progressive MS (PPMS) had a younger onset age. The most common type of MS is RRMS. Individuals had different relapse rates. Relapses are more common in patients who first develop the disease at a young age. In PPMS patients, oligoclonal band (OCB) positivity is higher than in RRMS patients. A considerable number of individuals exhibited aberrant visual evoked potential (VEP) even in the absence of visual complaints. Disease-modifying drugs decreased the disease frequency and severity. Patients who started these drugs after 1 - 2 relapses had good results. Conclusion: According to the findings, larger data sets are needed to completely characterize disease patterns. Given the rising prevalence of MS across India, it has become necessary to establish regional and national MS registries. J Neurol Res. 2023;13(1):43-49 doi: https://doi.org/10.14740/jnr750
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 imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".