O Complexo Quebra-Cabeça do Fenótipo Hipertrófico: Uma Abordagem Prática para o Clínico
Bibliographic record
Abstract
Left ventricular hypertrophy (LVH) represents a frequent observation in clinical practice. Nonetheless, the hypertrophic phenotype emerges as a common manifestation of diverse conditions, thereby presenting a diagnostic conundrum for clinicians. Differentiation among the etiologies of LVH is imperative for therapy decision-making, as different approaches must be implemented for distinct conditions, such as LVH secondary to loading changes, hypertrophic cardiomyopathy (HCM), or HCM mimics. In some instances, an erroneous or late diagnosis may lead to a progression of the underlying disease with worsening functional capacity, high morbidity and mortality. The rational use of cardiovascular multimodality imaging is of great importance when carried out in addition to a thorough clinical assessment and correlated with electrocardiographic findings, providing clues to fill the gaps, being, most of the time, the missing piece to solve this challenging puzzle. An integrative approach is of paramount importance for the evaluation of these patients, as they are often followed by several specialties, with varied systemic manifestations. Although a multidisciplinary team is needed for an optimized follow-up of these patients, the most important player in this journey is the clinician, whose mission is to bring together all the red flags and coordinate all the data for an assertive diagnosis. The objective of this review is to provide a pragmatic methodology, highlighting important clues for discriminating among the diverse conditions that result in LVH.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| 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".