Bibliographic record
Abstract
Cardiac computed tomography (CCT) can be used in acute chest pain settings and can identify some features of plaque vulnerability.1–6 The ones that can be considered somehow reliable to date are low or no calcification of the plaque, a large focal plaque burden that translates into the concept of positive remodelling, low density of the non-calcific component, and the ‘napkin ring’ sign.1–6 The last two features are more difficult to reproduce and to address. The concept of plaque disruption is a specific one; something that has to do with a certain imaging pattern where a rupture, a fissure, an intimal tear is visible. It relates with the ‘napkin ring’ sign since it entails that the contrast material penetrates into the layers of the plaque. Off course, intracoronary imaging would be the best approach. Several issues are present when dealing with coronary plaque imaging using CCT.7–10 We can mention the main ones: any density/attenuation quantification is affected by intravascular attenuation (limiting the impact of absolute value quantification); convolution kernel filtering and iterative reconstruction algorithm can affect the density/attenuation measurements; low-voltage protocols (nowadays widely adopted) are characterized by an increased noise, increased intravascular attenuation, and ultimately by a decreased soft tissue image resolution (also defined as contrast resolution), which is partially compensated by iterative reconstructions.7–11
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.011 | 0.012 |
| Insufficient payload (model declined to judge) | 0.007 | 0.005 |
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".