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Record W4391304441 · doi:10.1097/iop.0000000000002591

Disease Modulation Versus Modification: A Call for Revised Outcome Metrics in the Treatment of Thyroid Eye Disease

2024· review· en· W4391304441 on OpenAlexaff
Victoria S. North, Peter J. Dolman, James A. Garrity, Michael Kazim

Bibliographic record

VenueOphthalmic Plastic and Reconstructive Surgery · 2024
Typereview
Languageen
FieldMedicine
TopicOphthalmology and Eye Disorders
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsMedicineDiseaseThyroidNatural historyThyroid diseaseEye diseaseIntensive care medicineBioinformaticsPathologyInternal medicineSurgery

Abstract

fetched live from OpenAlex

PURPOSE: This perspective introduces the concepts of disease-modulating and -modifying therapy for thyroid eye disease and offers novel metrics for therapeutic outcomes. METHODS: A focused literature review was performed. RESULTS: Modulators are treatments that suppress disease symptoms whereas modifiers alter the natural history of a disease. Though many drugs are capable of exhibiting both effects, consideration of a drug's primary effect is useful when considering therapeutic options. For thyroid eye disease, corticosteroids and teprotumumab are effective at modulating many signs and symptoms of the disease, particularly those related to soft tissue inflammation. Orbital radiotherapy and rituximab have demonstrated effectiveness at durably modifying the natural history of thyroid eye disease. CONCLUSIONS: Outcome metrics should reflect the unique therapeutic objectives associated with disease modulation and modification. This conceptual framework should guide treatment of thyroid eye disease.

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 imitation

Not 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.

metaresearch head score (Codex)0.049
metaresearch head score (Gemma)0.049
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Evaluation · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Commentary · Consensus signal: none
Teacher disagreement score0.951
Threshold uncertainty score0.260

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0490.049
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0070.004
Bibliometrics0.0050.005
Science and technology studies0.0010.004
Scholarly communication0.0070.008
Open science0.0030.003
Research integrity0.0030.008
Insufficient payload (model declined to judge)0.0020.001

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.

Opus teacher head0.141
GPT teacher head0.396
Teacher spread0.255 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

Study designNot applicable
DomainEvaluation
GenreCommentary

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".

Quick stats

Citations8
Published2024
Admission routes1
Has abstractyes

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