Evaluating Anti-Hypertension Medications Usage on Patient Care Online Blogs
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.007 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it