Preference Trends for Antispasmodics Among Indian Healthcare Professionals: Results of a Cross Sectional Survey
Bibliographic record
Abstract
Objective: Understanding antispasmodics usage pattern among Indian healthcare practitioners (HCPs). Material and methods: HCPs were interviewed in person to understand preference of antispasmodics. The preference of formulation in acute/chronic pain, perceptions about attributes of antispasmodics, medicine recall and indications of different antispasmodics were noted. Results: Acute spasmodic pain is more common than chronic pain (61% vs 39% pediatrics; 58% vs 42% other specialties). In mild acute spasmodic pain tablet is used by 58% and in severe acute spasmodic pain injection is preferred by 55% HCPs. In mild and moderate chronic spasmodic pain, almost half of HCPs use tablet. Injection is used by 53% of HCPs for severe acute / chronic spasmodic pain. Injection is preferred for better efficacy by 67% HCPs. 80% healthcare practitioners use injection for quick onset of action. Tablets provide prolonged relief and are easy to administer according to 46% and 58% HCPs respectively. Camylofin plus paracetamol was the most common antispasmodic preparation recalled (91% HCPs). Conclusion: Spasmodic pain is common clinical condition. Antispasmodic injection is used in severe condition and quick onset of action while oral formulations are preferred for prolonged relief. Camylofin plus paracetamol is recalled by about nine out of ten HCPs.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
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".