Lack of sex bias in the referral letters for patients with inflammatory bowel disease: a mixed methods evaluation
Bibliographic record
Abstract
Abstract Introduction Women with inflammatory bowel disease (IBD) experience greater delays and misdiagnosis than men. Data from other conditions suggest that sex and/or gender bias in the process of referral to speciality care may contribute. Methods We undertook a mixed methods analysis of 120 referral letters to gastroenterology for people ultimately diagnosed with IBD in Calgary, Alberta. Letters were masked for patient sex and gender prior to analysis. Gastroenterologists who were masked to the objective of the study rated the quality of referral letters and triaged letters for urgency. Two study team members performed a Framework analysis to identify agentic (masculine) and commensal (feminine) adjectives, mentions of caregiving and work roles, and psychosocial history. After analysis, letters were unmasked and findings were compared by patient sex. Results There were 116 referral letters included in the analysis (n = 59, 50.9% for male patients). There were no differences in letter quality or triage urgency between male and female patients (median quality 4 [IQR 4-7] and 5 out of 10 [IQR 4-6], respectively, higher scores represent better quality; P = .37, and P = .44 for triage category). There was no difference in the use of adjectives and mention of caregiving or work roles, psychiatric history, or social history between letters for female and male patients. Conclusions This mixed methods analysis identified no difference in referral letter language, contents, or quality for female and male patients with IBD. Masked letters were triaged similarly to unmasked letters, suggesting an absence of sex and/or gender bias in the gastroenterology triaging process in our setting.
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 | 0.144 | 0.196 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 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".