Bibliographic record
Abstract
material.The written description of the interventions had 715 words.Our diagram (fig 3) shows immediately the cumulative nature of the experimental interventions.Although each intervention is complex, the comparison between successive interventions is relatively simple, each differing from the last by a single component.The year long interval between the control intervention and trial outcome also stands out, in contrast to the six month interval in the experimental arms. Advantages of using graphsGraphical depiction of an entire intervention allows its structure to be quickly understood.With the experimental and control interventions placed side by side on the diagram, differences between them-such as in the time elapsing between their delivery and the trial outcome-become obvious.We believe that the discipline of constructing a diagram will help at the design stage of a trial.By focusing attention on the components of the intervention, it prompts researchers to think through the structure, timing, and contents of each component in detail and to describe the components adequately.The exercise should help to ensure that the control intervention has been adequately considered and described and that the difference between the experimental arm and the control arm is appropriate for measuring the effect of the intervention.For the reader of the trial a graph will allow the details of an intervention to be quickly and easily grasped.Aspects that may be missed in a long verbal description stand out clearly, thus the differences between experimental and control interventions become obvious.The CONSORT trial flowchart has improved transparency and accurate reporting of the num-bers of participants at different stages of a study.We suggest that our proposed graphical method would similarly increase the clarity of reporting of complex intervention trials.We thank Paul Glasziou for helpful comments.
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 | 0.025 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.009 | 0.012 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.051 | 0.012 |
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".