In Reply: Guidelines for the Management of Severe Traumatic Brain Injury: 2020 Update of the Decompressive Craniectomy Recommendations
Bibliographic record
Abstract
To the Editor: We thank the authors1 for their thoughtful commentary related to the DECRA (DECompressive CRAniectomy) and RESCUEicp (Randomized Evaluation of Surgery with Craniectomy for Uncontrollable Elevation of intracranial pressure) randomized controlled trials. We are pleased to provide further thoughts on the controversy related to the extended Glasgow Outcome Scale (GOS-E) cut-point used in the RESCUEicp trial as well as the need to improve prognostication related to the performance of secondary decompressive craniectomy. In conjunction with our analysis2 of the DECRA and RESCUEicp trials, we performed a sensitivity analysis examining the influence of the cut-points on the primary endpoints of the 2 trials. Of note, DECRA’s trial registration specified the GOS-E cut-point to be used a priori, while RESCUEicp's trial registration did not specify a GOS-E cut-point. The results of our analysis (shown in Table) demonstrate that the distinct cut-points influence the results of the DECRA study but not the RESCUEicp study. The atypical dichotomization used in the RESCUEicp study thus did not influence its primary outcome though it does alter the magnitude of the nonsignificant effect seen, as highlighted in your letter. The difference in cut-points was an impediment to comparing the 2 studies; ultimately, we abandoned efforts to directly compare the 2 trials because of their important differences. TABLE. - Results of Chi-Square Analyses Comparing Primary Outcomes of DECRA with RESCUEicp, Measured by Dichotomous GOS-E for Two Cut-Points DECRA RESCUEicp GOS-E No significant difference between treatment groups No significant difference between treatment groups 1-3 unfavorable 4-8: favorable GOS-E1-4: unfavorable5-8: favorable Significantly more patients in unfavorable outcomes group No significant difference between treatment groups Gray cells denote the published dichotomization used in reporting the primary outcome measure (6-mo GOS-E) in each study. We emphatically agree with the need to better elucidate which patients stand a high probability of achieving good outcomes from secondary decompressive craniectomy.3 The success of the CRASH (Corticosteroid Randomization After Significant Head Injury)4 and IMPACT (International Mission for Prognosis And Clinical Trials in Traumatic Brain Injury)5 prognostic models has been a significant recent advance for traumatic brain injury care. Hopefully, these efforts have blazed a trail that other prognostic efforts will be able to follow. Notably, both prognostic models depend upon very large databases with over 9 000 severe traumatic brain injury patients to overcome the marked heterogeneity of this population. It is therefore anticipated that a similarly large dataset may be needed to generate robust prognostic information related to secondary decompressive craniectomy. Funding This study did not receive any funding or financial support. Disclosures The authors have no personal, financial, or institutional interest in any of the drugs, materials, or devices described in this article.
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How this classification was reachedexpand
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".