Bibliographic record
Abstract
Many important questions in medical research are investigated based on observational data, including “real-world evidence” studies.1 In fact, the vast majority of published medical research relies on data obtained from observational studies.2 This has been acknowledged by the increasing interest, focus, and acceptance of these types of studies. Although observational studies do have obvious weaknesses in the study design (in particular, inference of causality), it seems that the medical research paradigm has changed during the past few years. Focus has shifted from the downsides of observational studies to recognizing that they are important complements to randomized controlled clinical trials (RCTs). While biologic disease-modifying antirheumatic drugs (bDMARDs) have clinically proven efficacy in RCTs, results from RCTs may not be directly applicable to patients seen in a real-life setting, for example, because patients eligible for RCTs may have fewer comorbidities than those seen in daily practice. Multiple parallel cohort studies (one for each drug) may provide complementary information on … Address correspondence to Dr. L.E. Kristensen, The Parker Institute, Copenhagen University Hospital, Bispebjerg and Frederiksberg, Nordre Fasanvej 57, Road 8, Entrance 19, DK-2000 Frederiksberg, Copenhagen, Denmark. Email: lars.erik.kristensen{at}regionh.dk.
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 | 0.047 | 0.205 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.008 | 0.003 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.013 | 0.014 |
| Open science | 0.006 | 0.003 |
| Research integrity | 0.023 | 0.034 |
| Insufficient payload (model declined to judge) | 0.010 | 0.006 |
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".