Low-Value Clinical Practices in Adult Traumatic Brain Injury: An Umbrella Review
Bibliographic record
Abstract
Despite numerous interventions and treatment options, the outcomes of traumatic brain injury (TBI) have improved little over the last 3 decades, which raises concern about the value of care in this patient population. We aimed to synthesize the evidence on 14 potentially low-value clinical practices in TBI care. Using umbrella review methodology, we identified systematic reviews evaluating the effectiveness of 14 potentially low-value practices in adults with acute TBI. We present data on methodological quality (Assessing the Methodological Quality of Systematic Reviews), reported effect sizes, and credibility of evidence (I to IV). The only clinical practice with evidence of benefit was therapeutic hypothermia (credibility of evidence II to IV). However, the most recent meta-analysis on hypothermia based on high-quality trials suggested harm (credibility of evidence IV). Meta-analyses on platelet transfusion for patients on antiplatelet therapy were all consistent with harm but were statistically non-significant. For the following practices, effect estimates were consistently close to the null: computed tomography (CT) in adults with mild TBI who are low-risk on a validated clinical decision rule; repeat CT in adults with mild TBI on anticoagulant therapy with no clinical deterioration; antibiotic prophylaxis for external ventricular drain placement; and decompressive craniectomy for refractory intracranial hypertension. We identified five clinical practices with evidence of lack of benefit or harm. However, evidence could not be considered to be strong for any clinical practice as effect measures were imprecise and heterogeneous, systematic reviews were often of low quality, and most included studies had a high risk of bias.
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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 | 0.065 | 0.299 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.009 | 0.009 |
| Bibliometrics | 0.022 | 0.015 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".