The science of health technology assessment--clinical effectiveness of therapeutic interventions.
Bibliographic record
Abstract
Important information is not, and cannot, be available at the time a new drug enters the market. Delaying registration is not the answer because most of this information can only be obtained in a real life situation. Several types of postmarketing (phase IV studies) can be identified. The active pharmacovigilance cohort who allows large number of patients to be followed for long periods of time can answer questions about the incidence of rare events (less than one of 3000 patients). The prospective effectiveness cohort can answer questions on long term efficacy (more than two years). The simplified clinical trial, which implies randomly assigning patients and then following them with a 'naturalistic' protocol can answer questions about effectiveness (efficacy in real life). The drug use study is the only way to answer questions of key importance to drug plan managers such as 'which drug(s) is it going to replace?' and 'is it going to be used as first line or second line?'. Phase IV studies, which in some cases should be mandatory (conditional registration), are essential for the protection of the patients and the proper use of public funds to reimburse drugs.
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 | 0.121 | 0.296 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.002 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.001 | 0.009 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.013 | 0.004 |
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".