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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".