Bibliographic record
Abstract
la pared vascular, estasis e hipercoagulabilidad– los dos ultimos predominan como factores causales del tromboembolismo venoso (TEV). El termino trombofilia fue introducido por Egeberg en 1965 para describir una tendencia a la trombosis venosa en una familia noruega en la que demostro una deficiencia de antitrombina [1]. Desde entonces, con una mejor comprension de la cascada de la coagulacion junto con el desarrollo de complejas investigaciones moleculares, se ha ampliado la definicion de trombofilia, que ahora incluye cualquier anomalia que se asocia con el TEV. La trombofilia puede ser congenita o adquirida. La deficiencia de antitrombina y la disfibrinogenemia fueron las primeras trombofilias hereditarias descritas [1,2]. Desde 1993, tras el descubrimiento de la resistencia de la proteina C activada [3], las trombofilias hereditarias se clasifican habitualmente en dos grupos: las secundarias a una reduccion del nivel de algun inhibidor de la coagulacion y las debidas al incremento de los niveles o funcion de factores de la coagulacion [4]. Los principales estados trombofilicos adquiridos son el sindrome antifosfolipido (SAF), la hiperhomocistinemia (HHC) [5], el embarazo, el cancer, la utilizacion de contraceptivos orales o de tratamiento hormonal sustitutivo, la trombocitopenia inducida por la heparina, la enfermedad de Behcet y la enfermedad inflamatoria intestinal activa [6] (cuadro 1). Este articulo se centra en el sindrome antifosfolipido y en la HHC.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".