Diagnostic Accuracy of SPECT for Mild Traumatic Brain Injury
Bibliographic record
Abstract
PURPOSE: This study examines the diagnostic accuracy of brain perfusion SPECT for mild traumatic brain injury (mTBI). PATIENTS AND METHODS: A systematic review and meta-analysis was performed according to PRISMA guidelines (PROSPERO: CRD42023484636). Five databases were searched for studies evaluating brain perfusion SPECT in adult patients with mTBI (GCS 13-15). Study quality was assessed using a modified QUADAS-2 tool. A meta-analysis was performed to pool proportions of hypoperfusion abnormalities across brain lobes. RESULTS: Of 4735 records, 22 studies (5 longitudinal [40% high quality], 17 cross-sectional [24% high quality]) were included totaling 800 patients (mean age, 37.4 ± 12.6 years; 36.4% female). Meta-analysis of proportions indicated that the frontal lobe most frequently showed hypoperfusion on brain perfusion SPECT (pooled proportion 40.1% [95% confidence interval, 31.2% to 49.8%], 99/254, I2 = 54.5%), followed by the temporal lobe (26.1% [95% confidence interval, 19.9% to 33.6%], 68/254, I2 = 30.7%). Several studies found that hypoperfusion abnormalities were associated with neuropsychological findings. Also, brain perfusion SPECT could detect abnormalities not seen on MRI. Abnormalities in perfusion on brain perfusion SPECT may be more readily detected with a quantitative assessment compared with a visual assessment alone, although there appears to be no consensus on the optimal method for image interpretation. Evidence evaluating the sensitivity and specificity of brain perfusion SPECT for mTBI was limited. Using the GRADE framework, the evidence was rated as low. CONCLUSIONS: Although perfusion abnormalities can be seen in patients with mTBI, commonly in the frontal and temporal lobes, the findings are nonspecific and may derive from various factors. Ultimately, brain perfusion SPECT provides additional information for mTBI, but the final added value for the detection of mTBI is unknown.
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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 | 0.023 | 0.079 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.019 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".