Assessment of the ABC/2 Method of Epidural Hematoma Volume Measurement as Compared to Computer-Assisted Planimetric Analysis
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVE: Epidural hematoma volume (EDHV) is an independent predictor of prognosis in patients with epidural hematoma (EDH) and plays a central role in treatment decision making. This study's objective was to determine the accuracy and reliability of the widely used volume measurement method ABC/2 in estimating EDHV by comparing it to the computer-assisted planimetric method. METHODS: A data set of computerized tomography (CT) scans of 35 patients with EDH was evaluated to determine the accuracy of ABC/2 method, using computer-assisted planimetric technique to establish the reference criterion of EDHV for each patient. Another data set was constructed by randomly selecting 5 patients then replicating each case twice to yield 15 patients. Intra- and interobserver reliability were evaluated by asking four observers to independently estimate EDHV for the latter data set using the ABC/2 method. RESULTS: Estimation of EDHV using the ABC/2 method showed high intra- and interobserver reliability (intra-class correlation coefficient = .99). These estimates were closely correlated with planimetric measures (r = .99). But the ABC/2 method generally overestimated EDHV, especially in the nonellipsoid-like group. The difference between the ABC/2 measures and planimetric measures was statistically significant (p < .05). CONCLUSIONS: The ABC/2 method could be used for EDHV measurement, which would contribute to treatment decision making as well as clinical outcome prediction. However, clinicians should be aware that the ABC/2 method results in a general volume overestimation. Future studies focusing on justification of the technique to improve its accuracy would be of practical value.
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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 | 0.012 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".