Bibliographic record
Abstract
La enfermedad mamaria benigna engloba todos los trastornos mamarios no malignos, como tumores benignos, traumatismos, mastalgia, mastitis y galactorrea. Los tumores benignos dan lugar a alteraciones patologicas que no incrementan el riesgo de aparicion de una neoplasia maligna en la paciente, lesiones que entranan un riesgo ligeramente mayor y lesiones asociadas con un aumento del riesgo de desarrollo de cancer de mama de incluso el 50%. Tanto los trastornos mamarios benignos como los malignos pueden cursar con una masa palpable, engrasamiento cutaneo o eritema cutaneo, dolor, galactorrea e inversion o distorsion; o bien resultados anomalos en la mamografia de cribado en ausencia de hallazgos patologicos. Las herramientas que se utilizan para estudiar las alteraciones mamarias comprenden la exploracion clinica de las mamas, la mamografia y la ecografia. Este articulo aborda el papel del ginecologo en el mantenimiento de la salud de las mamas, la evaluacion clinica de sus alteraciones y el manejo de la enfermedad mamaria benigna.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.007 | 0.003 |
| Insufficient payload (model declined to judge) | 0.985 | 0.988 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".