�Estamos preparados para las intervenciones coronarias percut�neas preventivas?
Bibliographic record
Abstract
INTRODUCCIÓNCuando se habla de intervenciones coronarias percutáneas (ICP) preventivas, se hace alusión al tratamiento de placas de alto riesgo previo a la ocurrencia de eventos adversos.La decisión de administrar tratamiento preventivo se suele tomar cuando la tasa esperada de eventos de la enfermedad de base supera las posibles complicaciones a corto y largo plazo asociadas a la intervención.Un abordaje que también es aplicable al tratamiento de la enfermedad coronaria.Nuestro conocimiento sobre el avance de la placa ateroesclerótica y la identificación de placas de alto riesgo ha ido en aumento durante los últimos años, a la par que los avances tecnológicos de los dispositivos coronarios; todo ello ha alterado el equilibrio existente entre los riesgos de la enfermedad de base y los del tratamiento percutáneo.
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 | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.013 | 0.019 |
| 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".