Prevalence of Person-First Language in Idiopathic Intracranial Hypertension: A Systematic Review of Case Reports
Bibliographic record
Abstract
BACKGROUND: Person-first language (PFL) is a linguistic prescription, which places a person before their disease. It is considered an important tool to reduce stigma. However, PFL is not routinely used across the scientific literature, particularly in patients with overweight or obesity. Patients with idiopathic intracranial hypertension (IIH) face various stigmas through high rates of poverty, female gender, and frequent rates of comorbidities. Non-PFL language use intersects and worsen the health inequities faced by these patients. METHODS: A systematic review of case reports. MEDLINE and EMBASE were searched for all case reports with "pseudotumor cerebri" [MESH] OR "Idiopathic Intracranial Hypertension" as key word between January 1974 and August 2022. The primary criterion was the article's inclusion of patients with overweight or obesity. The secondary criterion was the article's discussion regarding obesity as risk factor. Articles not meeting primary or secondary criteria were excluded. RESULTS: Approximately 514/716 (71.8%) articles used non-PFL language. The publication year was predictive of non-PFL language: 1976-1991 (82.3%) vs 1992-2007 (72.3%, P = 0.0394) and 2008-2022 (68.3%, P = 0.0056). Non-PFL was significantly higher in obesity compared with other medical conditions (60.3% vs 7.3%, P < 0.001). The patient gender ( P = 0.111) and ethnicity ( P = 0.697), author's specialty ( P = 0.298), and primary English-speaking status ( P = 0.231), as well as the journal's impact factor ( P = 0.795), were not predictive of non-PFL. CONCLUSIONS: Most literature focused on IIH use non-PFL when discussing overweight or obesity, regardless of the patient's gender and ethnicity, journal's impact factor, senior author's specialty, and English-speaking status. Non-PFL use is much more common when discussing obesity compared with other medical conditions. Appropriate use of PFL can decrease stigma and, more importantly, decrease the intersectionality of health stigma faced by patients with IIH.
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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 | 0.004 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.016 | 0.019 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".