AYURVEDIC MANAGEMENT OF MANYASTAMBA WITH SPECIAL REFERENCE TO TORTICOLLIS: A CASE REPORT
Bibliographic record
Abstract
In this era of modernization and fast life, everybody is busy and living stressful life. Neck pain is common now a days, due to fast developing technical era people can’t concentrate on their proper regimens and facing problems like Manyasthambha. Manyastambhais defined under Nanatmaja Vatavyadhi. It is a disease where, the Vikruta Vata get lodges in the Manya Pradesha causing symptoms like Stambha and Shoola. Manyastambha can be corelated with symptoms of Torticollis. Objective: This single case study the efficacy of Valuka sweda, greeva basti and Pippalyadi Avapeedana Nasya in the management of Manyastambha. Methods: A case report of female patient where, 45-year-old with a chief complaint of Manyastambha and Manya shoola and restricted movements in the cervical joints. Two outcome measures were used for the assessment: Toronto Western Spasmodic Torticollis Rating Scale (TWSTRS) severity score. Assessment was conducted on the 0th and 8th day. Results: Torticollis can be effectively managed using Valukasweda, Greevabasti and Pippalyadi Avapeedana Nasya. There was clinically significant difference in pain intensity and Toronto Western Spasmodic Torticollis Rating Scale (TWSTRS) scores on the 0th day and 8th days. Conclusion: Toronto Western Spasmodic Torticollis Rating Scale (TWSTRS) scores on the 0th day and 8th days was reduced from 57 to 48. Hence, Valukasweda, Greevabasti and Pippalyadi Avapeedana Nasya in the management of Manyastambha.
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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".