Utilization of the 2017 diagnostic criteria for hEDS by the Toronto GoodHope Ehlers–Danlos syndrome clinic: A retrospective review
Bibliographic record
Abstract
The new 2017 diagnostic criteria for hypermobile Ehlers-Danlos Syndrome (hEDS) provide a framework for diagnosing hEDS but are more stringent than the previous Villefranche criteria. Our clinical experience at the GoodHope EDS clinic was that the 2017 criteria left many highly symptomatic patients without a diagnosis of hEDS. We conducted a retrospective cohort study to confirm our clinic experience and assess the accuracy of the 2017 diagnostic criteria for hEDS in patients who had a previous hEDS diagnosis based on the Villefranche criteria. Our study found that 15% (n = 20 of 131) of patients with a prior diagnosis of hEDS met the 2017 diagnostic criteria, and many of the traits used to distinguish hEDS were not significantly more frequent in patients who met 2017 criteria versus those who did not. In both groups objective systemic manifestations were found less frequently than subjective systemic manifestations. Beighton score (BS) as assessed by primary care practitioner was found to be higher than assessment by EDS practitioner in 81% (n = 74 of 91) of cases. Generalized joint hypermobility was confirmed in only 46% (n = 51 of 111) of patients who had a previous diagnosis of hEDS. Higher BS did not correlate with increased number of systemic manifestations in our cohort. Common comorbidities of hEDS were found with similar frequency in those who met 2017 criteria and those who did not. Based on our cohort, the 2017 hEDS diagnostic criteria require refinement to improve its diagnostic accuracy.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".