Localized and diffuse tenosynovial giant cell tumor: real-world results from a patient observational registry
Bibliographic record
Abstract
Background Tenosynovial Giant Cell Tumor (TGCT) is a rare, locally aggressive neoplasm that adversely impact patients’ physical function and quality of life (QoL). This cross-sectional analysis leverages real-world data from the TGCT Support Patient Registry to elucidate the patient experience with TGCT and the disease burden across healthcare systems.Research design and methods A total of 497 patients from 32 countries, 71.4% (n = 355) with diffuse-TGCT (D-TGCT), 18.9% (n = 94) with localized TGCT (L-TGCT), and 9.7% (n = 28) with unspecified TGCT were included in this cross-sectional analysis of the TGCT Support Registry.Results A majority of patients (61.2%, n = 304) were diagnosed by orthopedic/sports medicine surgeons, half (n = 248) were misdiagnosed prior to their TGCT diagnosis, and 32% (n = 278) of patients were diagnosed > 24 months following symptom onset. 79.1% (n = 393) of all patients had ≥ 1 resection and 63% of those patients reported ≥ 1 recurrence. Of those patients that had recurrence following resection, 59% had ≥ 2 recurrences. 23% of patients (n = 115) changed occupations or prematurely retired due to TGCT and the proportion of patients increased with > 2 surgeries.Conclusion Greater awareness of TGCT among HCPs is needed to facilitate diagnosis and referral to multidisciplinary teams is warranted to reduce recurrence rates, number of surgical interventions, and improve QoL.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".