Bibliographic record
Abstract
Because I review manuscripts and protocols for clinical trials, I observe a number of recurring issues. These issues mostly relate to the statistical analysis but also include appropriate characterization of the trial itself. Many reports of randomized controlled trials contain a table showing the baseline characteristics for each of the treatment groups, and some journals advocate use of significance tests to compare the groups at baseline. Indeed, the CONSORT (consolidated standards of reporting trials) statement supports inclusion of a baseline table; it also warns of the inappropriateness of using significance tests for comparison1. In statistics, hypothesis/significance tests concern population variables, and if indeed the allocation was randomized, a null hypothesis of no difference is true. Moreover, as Senn points out, an imbalance does not necessarily imply a problem with randomization; nor does a lack of imbalance prove randomization was successful2. Often, the results of baseline testing are used to decide which variables, if any, to include in an adjusted analysis of the outcome. Pocock, et al point out that baseline imbalance does not dictate the need for adjustment but rather it is the strength of the relationship between a baseline variable and the outcome3. Instead of p values, baseline comparability should be considered from the standpoint of clinical significance. Even then, imbalance should not be the criterion for inclusion in an adjusted model. In … Address correspondence to K.E. Thorpe, Dalla Lana School of Public Health, 155 College St., 6th floor, Toronto, Ontario M5T 3M7, Canada. E-mail: kevin.thorpe{at}utoronto.ca
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 | 0.759 | 0.882 |
| Meta-epidemiology (narrow) | 0.006 | 0.007 |
| Meta-epidemiology (broad) | 0.015 | 0.012 |
| Bibliometrics | 0.018 | 0.027 |
| Science and technology studies | 0.007 | 0.038 |
| Scholarly communication | 0.026 | 0.016 |
| Open science | 0.014 | 0.013 |
| Research integrity | 0.034 | 0.035 |
| Insufficient payload (model declined to judge) | 0.016 | 0.010 |
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".