ASPECTS: interobserver agreement between radiologist
Bibliographic record
Abstract
Introduction. The Alberta stroke programme early CT score (ASPECTS) was developed for a unified approach to the diagnosis of Acute Ischemic Stroke. ASPECTS is currently used as a standard method for assessment of ischemic volumes in the anterior cerebral circulation. However, the scale is not fully standardized, which is a source of intersubject variability. The purpose of the review is to gain an understanding the advantages and limitations of the ASPECTS scale, as well as the level of inter-expert and intra-expert agreement. Results. A literary analysis demonstrates most researchers have identified many factors that affect both the interpretation and assessment of the distribution of ischemic changes by ASPECTS. These signs are diverse and include a wide range of parameters: from methodological standardization to personal factors of experts. Also, studies on the effectiveness of the ASPECTS scale showed quite heterogeneous results, which reflect a wide degree of variability in inter-expert agreement. Conclusion. The ASPECTS is a systematic, reliable and practical method that is widely used in modern clinical practice. However, the possibility of variability of expert assessments is the main limitation of its application. The pronounced variety of results and the heterogeneity of intrasubject variability does not currently allow us to consider this scale as a truly reliable version of a standardized assessment and may affect the further treatment process. To solve this problem, it looks promising to introduce into clinical practice the methods of semi-automatic and automatic processing of CT images using artificial intelligence systems. But for the full acceptance of such systems into clinical practice, their wide clinical approbation on independent sets of different data is necessary.
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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 | 0.076 | 0.179 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".