The death of a neurotrauma trial lessons learned from the prematurely halted randomized evaluation of surgery in elderly with traumatic acute subdural hematoma (RESET-ASDH) trial
Bibliographic record
Abstract
Introduction: Acute subdural hematoma (ASDH) due to traumatic brain injury (TBI) constitutes an increasing global health problem, especially in the elderly population. Treatment decisions on surgical versus conservative management pose a neurosurgical dilemma. Large practice variation exists between countries, hospitals, and individual neurosurgeons, illustrating the presence of 'clinical equipoise'. The RESET-ASDH trial aimed to address this dilemma but was terminated prematurely due to insufficient patient recruitment. Research question: What factors may have contributed to the premature discontinuation of the RESET-ASDH trial? Materials and methods: The RESET-ASDH was a multicenter randomized controlled trial (RCT) comparing functional outcome at 1 year after early surgery or an initial conservative treatment in elderly patients (≥65 years) with a traumatic ASDH. Logs of registry data, medical-ethical approval timelines and COVID-19 related research documents were analyzed. Furthermore, non-structured interviews with involved clinical research personnel were conducted. Results: The concept of clinical equipoise was broadly misinterpreted by neurosurgeons as individual uncertainty, hampering patient recruitment. Also, the elderly target population complicated the inclusion process as elderly and their informal caregivers were hesitant to participate in our acute surgical trial. Moreover, the COVID-19 pandemic added additional hurdles like delayed medical-ethical approval, a decline in eligible patients and repeated trial halts during the peaks of the pandemic. Discussion and conclusion: The premature termination of the RESET-ASDH study may have been related to the trial's methodology and target population with an additional impact of COVID-19. Future acute neurosurgical trials in elderly may consider these challenges to prevent premature trial termination.
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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 | 0.098 | 0.175 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".