Altered cervical posture kinematics imposed by heavy school backpack loading: A literature synopsis (2009–2019)
Bibliographic record
Abstract
Background: Habitual school backpack carriage causes neuro-musculoskeletal vertebral, shoulder and hand pain; deviated posture compromised cardiopulmonary function and proprioception.Objective: Present a novel literature summary of the influence of backpack carriage associated with deviated cervical posture and compromised pulmonary function.Method: An electronic literature appraisal adopting the Preferred Reporting Items for Systematic Reviews, using Google Scholar, Science Direct, EMBASE, AMED, OVID, PubMed and Sabinet search engines, was instituted during 2009–2019. Key search words: schoolbag, backpack, carriage, cervical posture and children. The quality of the studies was assessed using the Downs and Black Appraisal Scale.Results: 583 records were initially identified which was reduced to 14 experimental and observational studies. A total of 1061 participants were included across the 14 studies, with an average age of 11.5 ± 1.3 years, body mass of 37.8 ± 6.6 kilograms (kg), height of 1.41 ± 0.05 meters (m), backpack mass of 5.2 ± 0.9 kg and percentage backpack mass to child’s body mass of 13.75%. The studies mean rating according to the Downs and Black Appraisal Scale was 76.3%. The average craniovertebral angle (CVA) was 53.9° ± 14.6° whilst standing without carrying a backpack was reduced to 50.4° ± 16.4° when loaded (p 0.05). Backpack loads carried varied from 5% – 30% of the participant’s body mass that produced a mean CVA decline of 3.5°.Conclusion: Backpack carriage alters cervical posture, resulting in smaller CVA and compromised pulmonary function. There is no consensus of the precise backpack mass that initiates postural changes. Girls’ posture begin changes when carrying lighter backpacks as compared to boys of the same age strata.
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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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.017 | 0.014 |
| 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) | 0.004 | 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".