Validating Techniques for Measurement of Cutaneous Neurofibromas
Bibliographic record
Abstract
OBJECTIVE: To assess the reliability and variability of digital calipers, 3D photography, and high-frequency ultrasound (HFUS) for measurement of cutaneous neurofibromas (cNF) in patients with neurofibromatosis type 1 (NF1). BACKGROUND: cNF affect virtually all patients with NF1 and are a major source of morbidity. Reliable techniques for measuring cNF are needed to develop therapies for these tumors. METHODS: Adults with NF1 were recruited. For each participant, 6 cNF were assessed independently by 3 different examiners at 5 different time points using digital calipers, 3D photography, and HFUS. The intraclass correlation coefficient (ICC) was used to assess intrarater and interrater reliability of linear and volumetric measurements for each technique, with ICC values >0.90 defined as excellent reliability. The coefficient of variation (CV) was used to estimate the minimal detectable difference (MDD) for each technique. RESULTS: Fifty-seven cNF across 10 participants were evaluated. The ICC for image acquisition and measurement was >0.97 within and across examiners for HFUS and 3D photography. ICC for digital calipers was 0.62-0.88. CV varied by measurement tool, linear vs volumetric measurement, and tumor size. CONCLUSIONS: HFUS and 3D photography demonstrate excellent reliability whereas digital calipers have good to excellent reliability in measuring cNF. The MDD for each technique was used to create tables of proposed thresholds for investigators to use as guides for clinical trials focused on cNF size. These criteria should be updated as the performance of these end points is evaluated.
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 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 | 0.073 | 0.152 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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.
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".