Quantomo: Validation of a Computer-Assisted Methodology for the Volumetric Analysis of Intracerebral Haemorrhage
Bibliographic record
Abstract
BACKGROUND: Volume measurements of intracerebral haemorrhage are prognostically important and are increasingly used in clinical trials to measure the effects of potential interventions. The purpose of this work is to establish the reliability of haematoma volume measurements obtained using a computer-assisted method called Quantomo (for quantitative tomography) and the ABC/2 method. Hypothesis Quantomo reliably detects smaller changes in intracerebral haemorrhage volume as compared with the ABC/2 method because computer-assisted volume measurements are tailored to measure the geometry of individual haematoma volumes whereas the ABC/2 method approximates all haematoma volumes as ellipsoids. METHODS: Thirty randomly selected computed tomography scans with intracerebral haemorrhage were measured by four raters a total of four times each (two sessions using Quantomo and two using the ABC/2 method). Interrater and intrarater reliability for both techniques were calculated simultaneously using a two-way random-effects analysis of variance model. The precision of intracerebral haemorrhage volume measurement was quantified as the minimum detectable difference with 95% confidence intervals. RESULTS: The median (first quartile and third quartile) intracerebral haemorrhage volume measurements of all rater and sessions for Quantomo were 32.7 ml (6.2 and 54.4 ml) and for ABC/2 40.7 ml (8.6 and 76.0 ml). Quantomo intracerebral haemorrhage volume measurements were more precise, having an inter- and intrarater minimum detectable difference of 8.1 and 5.3 ml, while the inter- and intrarater minimum detectable difference for ABC/2 were 37.0 and 15.7 ml. CONCLUSIONS: Quantomo is a computer-assisted methodology that is more reliable for quantifying intracerebral haemorrhage volume as compared with the ABC/2 method.
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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 | 0.020 | 0.051 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".