Bibliographic record
Abstract
We appreciate the acknowledgment by Xydakis et al. of our efforts to avoid potential sources of bias while conducting a very large, complex, multinational clinical trial of TBI.We relied on the Glasgow Coma Scale score as the primary entry criterion and the Glasgow Outcome Scale score as the primary end point, because these instruments have been shown to be robust over several decades.We agree that they have their limitations, and we support the concept of multidimensional approaches to classification of initial severity and of outcome. 1 However, how exactly these new pieces of information may best be used to improve TBI trial design and sensitivity remains to be determined.Xydakis et al. raise an important point regarding the need to evaluate patient subgroups on the basis of relevant criteria, whether biomarkers or imaging components.Indeed, we performed extensive prespecified subgroup analyses (see Table 2 of our article) and post hoc subgroup analyses.We found no hint of a trend toward efficacy in any of these analyses, in patients with diffuse injury, mass lesions, or traumatic subarachnoid hemorrhage or in those undergoing surgery.It would therefore seem unlikely that progesterone had any benefit in these subpopulations.It is of interest that Xydakis et al. suggest a potential role for characterizing patients on the basis of pathoanatomical findings from neuro-
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 | 0.001 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.007 | 0.009 |
| Insufficient payload (model declined to judge) | 0.008 | 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; 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".