Clinical Utility of SPECT Neuroimaging in the Diagnosis and Treatment of Traumatic Brain Injury: A Systematic Review
Bibliographic record
Abstract
PURPOSE: This systematic review evaluated the clinical utility of single photon emission computed tomography (SPECT) in traumatic brain injury (TBI). METHODS: After defining a PICO Statement (Population, Intervention, Comparison and Outcome Statement), PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) criteria were applied to identify 1600 articles. After screening, 374 articles were eligible for review. Inclusion for review was focus on SPECT in the setting of mild, moderate, or severe TBI with cerebral lobar specificity of SPECT findings. Other inclusion criteria were comparison modalities in the same subjects and articles in English. Foreign language articles, SPECT studies that did not include comparison modalities, and case reports were not included for review. RESULTS: We identified 19 longitudinal and 52 cross-sectional studies meeting inclusion criteria. Three longitudinal studies examined diagnostic predictive value. The first showed positive predictive value increases from initial SPECT scan shortly after trauma to one year follow up scans, from 59% to 95%. Subsequent work replicated these results in a larger cohort. Longitudinal and cross sectional studies demonstrated SPECT lesion localization not detected by CT or MRI. The most commonly abnormal regions revealed by SPECT in cross-sectional studies were frontal (94%) and temporal (77%) lobes. SPECT was found to outperform both CT and MRI in both acute and chronic imaging of TBI, particularly mild TBI. It was also found to have a near 100% negative predictive value. CONCLUSIONS: This review demonstrates Level IIA evidence (at least one non-randomized controlled trial) for the value of SPECT in TBI. Given its advantages over CT and MRI in the detection of mild TBI in numerous studies of adequate quality, and given its excellent negative predictive value, it may be an important second test in settings where CT or MRI are negative after a closed head injury with post-injury neurological or psychiatric symptoms.
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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 | 0.009 | 0.046 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.008 |
| 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) | 0.004 | 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".