Clinical Utility of SPECT Neuroimaging in the Diagnosis and Treatment of Traumatic Brain Injury: A Systematic Review
Bibliographic record
Abstract
Abstract Background The most common assessment modalities to determine the level of injury following a traumatic brain injury (TBI) includes computerized tomography (CT) scans and/or magnetic resonance imaging (MRI). Evidence is mixed as to whether single photon emission computed tomography (SPECT) is specific and accurate in identifying TBI. Objectives This study systematically assessed recent evidence of the clinical utility of SPECT in the diagnosis of TBI and examined the diagnostic accuracy of SPECT in TBI and its performance in comparison to other imaging modalities (e.g., CT and MRI). Methods PubMed, MEDLINE, and Embase databases were systematically searched for published articles from December 2012 to July 2022. Randomized controlled trials (RCTs) and observational studies published in English that used SPECT to evaluate patients with all severity of TBIs were eligible for inclusion. Titles and abstracts were screened, and 111 selected full-text articles were independently screened based on predefined inclusion/exclusion criteria (guided by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses; PRISMA) and assessed for quality using the Newcastle-Ottawa Scale. Results Fourteen eligible studies, all observational, reporting location of lesions on brain SPECT were included, reporting data from 21632 participants of which 20,746 participants were from one study; the remaining 886 participants were from the remaining13 studies. The heterogeneity of the data precludes a meta-analysis. There was no consensus among experts from the thirteen smaller studies; however, the largest study indicated that the specificity of visual readings was 54%. In particular, abnormalities and brain perfusions may lead to false positives. Quantitative analysis theoretically increases the reliability of findings for brain SPECT, but error rates are unknown and not published. Conclusion There is a lack of evidence to support the clinical utility of brain SPECT for the diagnosis and treatment of TBI.
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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 | 0.010 | 0.046 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.011 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".