Can electronic screens influence head and neck posture in adolescents? A systematic review
Bibliographic record
Abstract
ABSTRACT This systematic review aims to identify whether electronic screens can influence head and neck posture in adolescents. This study was registered in PROSPERO and the databases used were EMBASE, LILACS, SciELO, PEDro, PubMed, and Scopus, with no language or publication date limitations. The keywords used were posture, neck, and adolescents. A total of 1,997 articles with duplicates were found, 1,858 articles were excluded after title reading and 65 after abstract reading. During the analysis of the full texts, 22 were excluded because they addressed individuals with an average age of less than 15 or more than 19 years, 10 did not refer to technology use, and three only evaluated symptomatic individuals, therefore, only four articles were reviewed. The methodological quality of the studies was defined according to the Newcastle Ottawa Quality Assessment Scale, with three being classified as good methodological quality and analyzing posture when using a computer, and one with poor quality that analyzed posture when using a smartphone. Therefore, regarding smartphone use, considerations are limited. Overall, computer use is not responsible for postural changes in the head and neck of adolescents; however, more studies are needed to confirm this conclusion.
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 | 0.006 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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".