Reply: When is the time right for a phase III clinical study in spinal cord injury (P = 0.05)?
Bibliographic record
Abstract
Sir, We thank Drs Kramer and Curt for their comments and appreciate the opportunity to respond. They highlight a very important issue pertaining to clinical trial design: identification of an appropriate target population best suited to establish interventional efficacy. While some would argue that patient recruitment should be restricted as little as possible, others maintain that tight sampling characteristics reduce variance and improve study discrimination. Both philosophies have merit; however, in the latter approach lies risk that study results may not be generalized to a ‘real-world’ population. We attempted to address this dilemma in our Phase II trial (Casha et al., 2012) through stratification of subjects with motor complete and motor incomplete spinal cord injuries in our primary randomization scheme. Our principle clinical outcome of motor recovery made it intuitive to focus on patients with the most potential to demonstrate improvement in this regard—i.e. motor incomplete patients. This population was also key as it is able to detect both positive and negative changes in the American Spinal Injury Association (ASIA) examination. Notwithstanding that, a possible treatment effect in complete patients was also recognized as important to the spinal cord injury community. Hence both groups were included, but in an independent manner. Post hoc, because of poor recovery overall in patients with thoracic cord injuries, it became attractive to regard them separately from those with cervical trauma. These observations were not unexpected as not only is the ASIA motor examination unable to detect segmental change in the thoracic spinal cord but also thoracic spinal cord injury is typically more severe due to (i) high forces required to disrupt this region of the spine; (ii) relatively small canal:cord ratio; and (iii) tenuous blood supply to the spinal cord itself.
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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 | 0.022 | 0.121 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.040 | 0.046 |
| Insufficient payload (model declined to judge) | 0.008 | 0.008 |
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".