Calcium score: what do the most reliable guidelines recommend? An analysis using the G-TRUST tool
Bibliographic record
Abstract
INTRODUCTION: In 2023, cardiovascular disease was the leading cause of death worldwide. Various risk calculation tools based on risk factors can be used to estimate this risk. Calculating the coronary calcium score should allow us to assess this risk at an individual level. There is no consensus in the various good clinical practice guidelines (CPG) on the use of this score. The aim of this study was to assess the reliability of the various CPGs for the use of the calcium score in primary prevention. METHODS: CPGs published between 2018 and 2023 whose recommendations included advice on the use of CSC in primary prevention cardiovascular risk assessment for the general population was searched via Pubmed. The G-TRUST evaluation grid was then applied to the CPGs to determine which fell into the "reliable and relevant" category. RESULTS: 467 publications were identified via Pubmed. Only seven met the inclusion criteria. Of these seven CPGs, only two obtained an overall score of "reliable and relevant." The other five were assessed as "not usable" because of the risk of conflicts of interest, the absence of a systematic review, or the absence of patients' opinions and wishes. DISCUSSION: The two CPGs selected as reliable and relevant recommended that the CSC should not be used to assess cardiovascular risk, while the five classified as "not usable" recommended its use. G-TRUST is a tool which assesses the quality of the design of a recommendation and not the quality of the guidelines they propose.
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 | 0.082 | 0.453 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.008 |
| Bibliometrics | 0.027 | 0.027 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".