Strength of the Evidence Generated From Reports of Clinical Research
Bibliographic record
Abstract
SUMMARy Positive or negative findings in clinical research may represent true‐positive or true‐negative results, which reflect a relationship that does or does not exist in the reference population of interest. Alternatively, positive or negative findings in clinical research may represent false‐positive or false‐negative results, which are due to chance, bias, and/or confounding factors. If such alternative explanations for the results of a study have been ruled out, a valid statistical association is reported. A judgment of a cause‐and‐effect relationship is based on the totality of all available evidence, and it can also be made from a large, double‐blind, and correctly conducted and analyzed randomized controlled trial.
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 | 0.349 | 0.765 |
| Meta-epidemiology (narrow) | 0.005 | 0.004 |
| Meta-epidemiology (broad) | 0.022 | 0.022 |
| Bibliometrics | 0.033 | 0.024 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.018 | 0.011 |
| Open science | 0.011 | 0.010 |
| Research integrity | 0.014 | 0.008 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".