Biological Sex Inequality in Rheumatology Wait Times During the COVID-19 Pandemic
Bibliographic record
Abstract
Objective To examine the effect of biological sex on wait times to first rheumatology appointment in a central triage system before and during the coronavirus disease 2019 (COVID-19) pandemic. Methods Deidentified data of all referred patients between November 2019 and June 2022 were extracted from the electronic medical record. Variables, including time from referral to first appointment, biological sex, referral period, urgency status, age, and geographic location were collected and analyzed. Results Twelve thousand eight hundred seventeen referrals were identified. Wait times increased by 24.23 days in the peri-COVID period (P< 0.001). In the pre-COVID period, there was no significant difference in wait times by biological sex or age. Triage urgency was a predictor of wait time, with semiurgent referrals seen 8.94 days (95% CI −15.90 to −1.99) sooner than routine referrals and urgent referrals seen 25.42 days (95% CI −50.36 to −0.47) sooner than routine referrals. In the peri-COVID period, there was a significant difference in wait time by biological sex with women waiting on average 10.03 days (95% CI 6.98-13.09) longer than men (P< 0.001). Older patients had shorter wait times than younger patients, with a difference of −4.64 days for every 10-year increase in age (95% CI −5.49 to −3.78). Triage urgency continued to be a predictor of wait time. Conclusion Women and younger patients appear to have been affected by wait time increases during the COVID-19 pandemic. This finding should be further investigated to determine its pervasiveness across other specialities and to better understand the underlying cause of this finding.
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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 | 0.007 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".