Bibliometric analysis of the literature relating to silicone hydrogel and daily disposable contact lenses
Bibliographic record
Abstract
Publication metrics are derived for the fields of silicone hydrogel (SH) and daily disposable (DD) contact lenses. A search of the Scopus database for papers in the fields of SH and DD contact lenses found 979 SH and 291 DD papers. Subject-specific h-indices for SH lenses (hSH-index) and DD lenses (hDD-index) were derived, in relation to five categories – authors, institutions, countries and journals – to serve as measures of impact. A short list of the most impactful entities was generated for each of the above five categories in the SH and DD fields. A paper entitled “Soft contact lens polymers: An evolution” by Nicholson and Vogt was the most highly cited article (495 citations) in both SH and DD fields. The most impactful entities for the SH and DD fields were: authors – Lyndon Jones (hSH = 33) and Philip Morgan (hDD = 15); institutions – the University of Waterloo (hSH = 37) and the University of New South Wales (hDD = 15); countries – the United States (hSH = 45) and the United Kingdom (hDD = 24); and journals – Optometry and Vision Science (hSH = 33) and Contact Lens and Anterior Eye (hDD = 17). Overall, the SH field (hSH = 64) is far more impactful than the DD field (hDD = 34). Impactful papers, authors, institutions, countries and journals in the SH and DD fields are identified. Optometry is revealed as the leading profession in relation to SH and DD publications.
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 | 0.009 | 0.071 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.283 | 0.333 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".