The use of non-person-first language in neuro-ophthalmology referrals
Bibliographic record
Abstract
Objective To investigate the prevalence of non-person-first language (PFL) in neuro-ophthalmology referrals to a single tertiary ophthalmology clinic. Design Retrospective cross-sectional study. Methods Participants included neuro-ophthalmology patients seen for their initial visit from July 2018 to December 2022. Ten randomly selected referrals from each day were screened. Non-PFL was further categorized as per American Medical Association and American Psychological Association guidelines. Associations between non-PFL and patient age and gender, referring provider gender and specialty, and year and length of referral, were evaluated using the χ 2 test. Results A total of 2105 referrals were included in the study and 81 (3.8%) used non-PFL, such as referring to a person with glaucoma as a "glaucoma patient". Error types included general (38.3%), stigma (25.9%), diabetes (19.8%), disability (13.6%), and obesity (2.5%). Non-PFL was significantly more likely in long referrals compared with medium and short referrals (9.9% vs 3.1% vs 1.3%; p < 0.001). Referral year was predictive of non-PFL ( p = 0.0006), with a significant increase from 2018 (1.7%) to 2021 (6.1%) and decrease in 2022 (2.6%). Patient age was also predictive of non-PFL ( p = 0.0359), with the highest prevalence among patients 40–69 years old (5.4%). Patient gender ( p = 0.3350), referring provider gender ( p = 0.3571), and referring provider specialty ( p = 0.1280) were not predictive of non-PFL. Conclusions The highest proportions of non-PFL errors made were general and stigma errors. Non-PFL use was most prevalent in 2021, most commonly in referrals for patients aged 40–69 years. There exists a need for ongoing education and awareness around PFL use in physician–physician communication to enhance inclusive, nonstigmatizing care for neuro-ophthalmology patients.
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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 | 0.001 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| 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 | 0.000 |
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".