Evaluating Anti-Hypertension Medications Usage on Patient Care Online Blogs
Bibliographic record
Abstract
Throughout history, the area of drug discovery and development has been a financial strain due to the associated high costs.In order to offset this financial burden, drug companies are continually increasing the price of medications to consumers.Some consumers remain unaware of the types of medications available on the market and the gap in cost between these types.The two main types of medications available on the market are: 1) Generic drugs and 2) Brand name drugs.The purpose of this paper is to examine the similarities and difference of generic and brand name drugs from patient's reviews at major medications blogs like dugs.com.A subjectivity sentimental analysis framework has been developed that can effectively score these reviews without going into the complexities of using natural language or machine learning approaches.The developed framework use a well-known rule based subjectivity API known as VADER besides an effective web crawler.Results of this analysis shows more sentiments are with the generic antihypertension drugs compared to the brand drugs.The validation of these results was based on Google Trends.More concrete analysis of the results on wider list of medications as well as more blogs is left to our future work.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".