Bibliographic record
Abstract
Introduccion: Los aparatos electronicos que contienen pantalla digital son aquellos los que transmiten ondas de emision de luz LED, segun alta definicion pero con intensidad que al atravesar la retina afecta simultaneamente la funcion visual, conocido en una nueva denominacion como sindrome visual digital. Objetivo: Identificar el tiempo adecuado frente a la pantalla digital, por los efectos visuales que llegaran a producir, mediante el analisis de articulos cientificos indexados en los ultimos 5 anos, para contribucion hacia el medico de primer nivel de atencion. Metodo: Se aplico el estudio sistematico de revision de articulos cientificos indexados en revistas virtuales publicadas dentro de los ultimos 5 anos, estas revistas o paginas web son: Scielo, PUBMED, COCRHANE, DIALNET, MEDLINE, y, Scopus. Se han identificado cerca de 24 articulos que cumplen con la tematica propuesta conectandolos por la aplicacion de Mendeley, para al ser procesados son debidamente citados con las normas Vancouver. Conclusion: Que el tiempo que se estima sea suficiente para dejar danos que afectan la funcion visual es de 30 horas durante la semana o un promedio de 4 horas al dia. Que dentro de los efectos mas importantes son: cataratas, diplopia, sindrome de ojo enrojecido, tension ocular, fatiga ocular, ojo seco, miopia, hipermetropia y astigmatismo.
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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.034 | 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".