Validation of farsi translation of the ocular surface disease index
Bibliographic record
Abstract
PURPOSE: To develop and validate a Farsi version of Ocular Surface Disease Index (OSDI) for the Iranian population. METHODS: This study was a translation and cross-cultural adaptation and validation of Farsi version of OSDI. Four bilingual (English-Persian) individual including three physicians and one native English teacher were asked to translate the original English OSDI questionnaire in Farsi. Following back and forth translation, integration and pilot check, the translation team came to consensus on translation. Consecutive patients visited in ophthalmology clinic, underwent comprehensive general ophthalmology exam and specific assessments for dry eye including non-anesthetic Schirmer's test, fluorescein tear break-up time, Fluorescein and Rose Bengal staining and Farsi OSDI (F-OSDI). F-OSDI was again rechecked within 2-7 days after the examination. RESULTS: Forty-four participants were enrolled into study. Thirty-two (72.7%) were male and 12 (27.3%) female. Mean age of participants was 45.5 (SD = ±15.97, range = 18-80) years. Twenty five percent were less than 31 years old and 10% percent older than 65. The cronbach's alpha for the questionnaire was 0.807. Questions number 7, 8 showed excellent, and question12 showed good internal consistency, respectively. There was a significant correlation between all pre measures and post assessments. CONCLUSION: The obtained F-OSDI showed acceptable internal consistency and test-retest reliability. This F-OSDI could be used for assessment of dry eye, ocular surface discomfort and quality of life in Iranian and Farsi speaking populations.
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 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".