PREreview of "How to Save Eyesight: Recommendations from a Review Studies"
Notice bibliographique
Résumé
This Zenodo record is a permanently preserved version of a PREreview. You can view the complete PREreview at https://prereview.org/reviews/17563336. Preprint Review How to Save Eyesight: Recommendations from a Review Studies. DOI: 10.20944/preprints202509.2234.v1 General Overview The paper provides a narrative review of studies concerning myopia in children, emphasizing ergonomics, environmental factors, and behavioral influences such as reading distance, posture, and screen time. The paper synthesizes findings primarily from Asian populations, where myopia prevalence is highest, to propose global recommendations for prevention. Strengths Timely and relevant topic. The rising prevalence of childhood myopia, especially post-COVID-19, is a pressing global health issue. Comprehensive literature inclusion: The paper summarizes 22 studies of varying study designs, mostly from high-quality journals, addressing different dimensions of myopia: genetics, posture, sleep, school environment, and preventive interventions. Practical recommendations: the discussion section translates evidence into actionable prevention strategies, such as, limiting near work duration time, adjusting school furniture ergonomically, encouraging outdoor time, encouraging adequate sleep and reducing academic pressure. Cross-disciplinary insight: the integration of physiotherapy and biomedical engineering perspective gives the paper a unique focus on ergonomics, often underrepresented in ophthalmic research. Weaknesses and Areas for Improvement 1. Methodological Transparency: the methodology does not demonstrate the rigor expected of a systematic or meta-analysis. The study selection process is not supported by a PRISMA flow diagram or inclusion/exclusion criterion beyond age group. There's no quality assessment of the included studies (e.g., using GRADE or NEWCASTLE-Ottawa tools). 2. Data Synthesis: The results are narrative summaries without quantitative synthesis (e.g., pooled prevalence or Odds ratios). The meta-analysis data presentation mentioned in the abstract is not actually performed. This may mislead readers about the paper's analytical depth. The meta-analysis data including pooled estimates and forest plots 3. Regional Bias: The evidence base is almost entirely Asian (especially Chinese). While justified by high prevalence, the paper generalizes findings globally without considering sociocultural or environmental differences in non-Asian contexts. Formatting and Language 1.The manuscript has several grammatical and structural errors. For example the title "Recommendations from a Review Studies" should be revised for grammatical correctness to for example "Recommendations from Review Studies" or A Review of Strategies to Save Eyesight". 2.Scientific Depth: The discussion lacks critical comparison between conflicting studies (e.g., reading posture-lying down vs. sitting). 3.Citation and Referencing: Some references are incomplete or not consistently formatted (e.g., missing page ranges, inconsistent capitalization). The authors could have utilize reference management tool e.g., Zotero or EndNote to streamline the references. Recommendations for Improvement 1. Clarify Review Type: structure the paper appropriately to be either systematic review, scoping review or narrative synthesis. And include a clear search strategy, inclusion/exclusion criteria, and data extraction framework. 2. Improve Analytical Rigor: Add a table summarizing key study characteristics (country, sample size, age, outcome, quality rating). Consider conducting a basic quantitative synthesis or provide forest plots to visualize trends. 3. Enhance Scientific Discussion: Discuss casual pathways (e.g., why outdoor light exposure protects vision). Highlight policy implications (e.g., school design, screen use guidelines). Compare with non-Asian data to enhance generalizability. 4. Reformat manuscript according to journal guidelines, add missing figures and ensure consistent reference formatting. Publication: Major Revision before submission to peer-reviewed journal. Reviewers: Murtala Haruna Bawa Allah https://orcid.org/0000-0002-1435-4032 Juliet Gamuchirai Nyamasve https://orcid.org/0009-0001-3152-8567 Competing interests The authors declare that they have no competing interests. Use of Artificial Intelligence (AI) The authors declare that they did not use generative AI to come up with new ideas for their review.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,094 | 0,382 |
| Méta-épidémiologie (sens strict) | 0,004 | 0,004 |
| Méta-épidémiologie (sens large) | 0,011 | 0,012 |
| Bibliométrie | 0,017 | 0,014 |
| Études des sciences et des technologies | 0,003 | 0,004 |
| Communication savante | 0,014 | 0,015 |
| Science ouverte | 0,011 | 0,007 |
| Intégrité de la recherche | 0,014 | 0,010 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,110 | 0,063 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».