Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Randomized trials are an important method for deciding whether integrative oncology therapies do more good than harm. Many investigators do not pay sufficient attention to randomization procedures, and several studies have shown that only a fraction of trial reports describe randomization adequately. The purpose of randomization is to prevent selection bias: randomization procedures must therefore ensure that researchers are unable to predict the group to which a patient will be randomized until the patient is unambiguously registered on study; moreover, researchers must be unable to change a patient's allocation after the patients are registered. The use of telephone randomization and opaque envelopes has been suggested as a good randomization method, but both can be subverted. Randomization should be conducted either by a pharmaceutical company, which sends blinded medication to the hospital pharmacy, or by a secure, password-protected database system. Computer randomization can easily incorporate extensions of randomization, such as blocking, stratification, and minimization, which can help ensure balance between groups.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.197 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.007 | 0.015 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it