P.142 The effect of modern technology on cervical spine biomechanics. Literature review
Bibliographic record
Abstract
Background: The use of smartphones has increased drastically over the last decade. Improper posture, and excessive use have raised concerns about their effect on cervical spine health. Methods: -MEDLINE database was searched for articles using the keywords: neck pain, musculoskeletal symptoms, cervical spine, cervical -biomechanics, mobile phone, cell phone, smart phone, smartphone, mobile device, touchscreen phone. Full-text Articles from 1990 to 2017 were included. Statistical comparisons and tables are provided when appropriate. Results: 43 articles were included for review. First article was published in 2002. Majority of studies were published between 2010 – 2017 (36 vs. 5 in 2000-2010). Studies included were of cross-sectional, experimental, or systemic review design. No longitudinal studies were identified. We categorized articles into 5 subgroups; we found 14 biomechanical studies, 10 electromyographic studies, 5 ergonomical studies, 14 clinical studies, and no surgical studies. Conclusions: Text-neck posture leads to significant changes in cervical spine biomechanics. Increased compressive load, antero-posterior shear load, and high cervical extensor muscles activity were associated with forward flexed neck posture adapted by smartphones users. Neurosurgeons need to take the abnormal posture and load distribution into consideration when planning for surgical interventions, especially in young adults with history of excessive use of smartphones.
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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.011 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 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".