Orbital decompression following treatment with teprotumumab for thyroid eye disease
Bibliographic record
Abstract
OBJECTIVE: To quantify the observed decrease in orbital decompressions being performed at one tertiary care institution and to determine the rate and predictive factors of orbital decompression surgery following treatment with teprotumumab for thyroid eye disease. METHODS: Epic's SlicerDicer program was used to analyze recent trends in the overall number of thyroid eye disease (TED) patients evaluated in the oculoplastic surgery department, as well as usage trends of CPT codes 67445 (lateral orbitotomy with bone removal for decompression) and 67414 (orbitotomy with removal of bone for decompression). A retrospective chart review of active moderate-to-severe TED patients treated with teprotumumab was performed at a single tertiary care center. The main outcome measure was whether or not patients underwent bony orbital decompression surgery following treatment with teprotumumab. The SlicerDicer search demonstrated stable usage of CPT codes 67445 and 67414 from 2016 to 2019, followed by a significant decrease from 2020 to 2023, over a background of increasing numbers of TED patients evaluated in clinic. Following teprotumumab therapy, 25% of patients and 18% of orbits underwent bony decompression. Surgically decompressed patients had higher pre- and post-teprotumumab exophthalmometry measurements compared with patients who did not undergo bony decompression. Average time to decompression following conclusion or cessation of teprotumumab therapy was 12.6 months. CONCLUSION: While the number of TED patients treated at one tertiary care center has risen over recent years, the number of orbital decompression surgeries has declined. Orbital decompression, however, is still needed in select patients after treatment with teprotumumab.
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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 | 0.000 | 0.002 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".