Bibliographic record
Abstract
Randomized controlled trials are criticized for the difficulty of translating the results obtained to healthcare and clinical practice. The populations of patients enrolled in the studies are supposed to be too different from the patients encountered daily by the health professionals. The solution of these problems should come from the so-called real world evidence: data generated by the patients, from diseases registries, from electronic medical records or, in perspective, from big data. However, all evidence is real world evidence - whether RCT or observational or any other source. In a sense the more real you want to be one the more is the risk of bias. The answer to the problems of clinical research can only come from the careful evaluation of the evidence, assessing how reliably the evidence support all the factors that can determine a recommendation or a decision. These factors include the importance of a health problem, the balance between health benefits and risks, the values that people attach to outcomes, resource use, equity, acceptability and feasibility. Ideally, the evidence should be exposed transparently in a GRADE framework evidence to decision (EtD).
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 | 0.259 | 0.670 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.009 |
| Bibliometrics | 0.012 | 0.006 |
| Science and technology studies | 0.002 | 0.011 |
| Scholarly communication | 0.023 | 0.020 |
| Open science | 0.006 | 0.007 |
| Research integrity | 0.016 | 0.015 |
| Insufficient payload (model declined to judge) | 0.047 | 0.011 |
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".