Tiempo para quedar en embarazo: consideraciones generales y metodológicas.
Bibliographic record
Abstract
Desde la segunda mitad de la década de los 80, el tiempo para quedar en embarazo se está utilizando en epidemiología ambiental para explorar efectos adversos sobre la fecundidad.En este artículo se revisa la racionalidad de los estudios de tiempo para quedar en embarazo y los principales elementos que se deben considerar en el diseño de un estudio de este tipo: tipo de estudio, poblaciones, tamaño de muestra, medición del evento, análisis estadístico y sesgos.Se sugieren algunas pautas por tener en cuenta para la realización de este tipo de estudios.Palabras clave: infertilidad, métodos epidemiológicos, diseño de investigaciones epidemiológicas, análisis de supervivencia. Evaluation of time-to-pregnancy as a measure in environmental epidemiologySince the late 80's, time to pregnancy has been used in environmental epidemiology to explore adverse effects of different exposures.The advantages of this measure and additional elements to be considered in the performance of this type of studies are reviewed.Study design includes the following steps: population selection, sample size, outcome measurement, statistical analyses and sources of bias.Guidelines were suggested for the properly development of this type of study.
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 | 0.016 | 0.058 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 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".