Bibliographic record
Abstract
T he term 'hypertrophy' means an abnormal growth (eg, cardiac hypertrophy is understood as an abnormal growth of cardiac mass due to an increase in the size of myocytes).A second meaning of the term 'physiological' is, according to the dictionary, normal.Taking the above interpretations into consideration, we arrive at an amusing semantic incompatibility: cardiac physiological hypertrophy would mean 'normal abnormal growth of the heart'.Authors who coined this term most likely intended to refer to cardiac hypertrophy achieved by physiological stimuli.The next logical question is whether there a specific subtype of cardiac hypertrophies mislabelled as 'physiological' hypertrophies.For example, in a recent authoritative review, Tomanek (1) divides hypertrophies into the following groups: hypertrophies due to: pressure overload; volume overload; hyperthyroidism; and cardiomyopathies.However, the term 'physiological hypertrophy' is avoided.Most publications using the term 'physiological hypertrophy' concern changes in the heart after chronic physical exercise.Occasionally, it is also used to describe cardiac changes during pregnancy or in hyperthyroidism.Several functional, morphological and/or biochemical differences between 'physiological hypertrophy' and the remaining types of 'pathological' cardiomegalies are reported.All of these changes are usually considered to be reversible.Generally, the distinctions are presented as differences between a 'good' hypertrophy and a 'bad'
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.019 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.005 | 0.010 |
| Insufficient payload (model declined to judge) | 0.005 | 0.004 |
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".