Interreader Reliability in Assessment of Nailfold Capillary Abnormalities by Beginners: Pilot Study of an Intensive Videocapillaroscopy Training Program
Bibliographic record
Abstract
OBJECTIVE: To test the learning curve of rheumatologists with different experience in videocapillaroscopy (VCP) attending an intensive training program focused on interpretation of the main capillary nailfold abnormalities, the scleroderma (systemic sclerosis, SSc) pattern, and the normal pattern, and to determine their interreader agreement with an experienced investigator. METHODS: Five investigators (1 senior, 1 junior, and 3 beginners) participated in the exercise. The study was composed of 2 steps. First, an independent investigator selected representative VCP images of normal patterns and capillary abnormalities. The second step included the training program, which ran 4 hours per day for 7 days. The senior rheumatologist taught investigators to recognize and interpret the normal pattern, the capillary abnormalities, and the different types of SSc pattern. These abnormalities were considered: homogeneously enlarged capillaries, giant capillaries, irregularly enlarged capillaries, microhemorrhages, neoangiogenesis, avascular areas, and capillary density. RESULTS: A total of 300 VCP images were read from all the investigators. Both κ values and overall agreement percentages of qualitative and quantitative assessments showed progressive improvement from poor to excellent from the beginning to the end of the exercise. The sensitivity and specificity of the participants in the assessment of SSc pattern at the last lecture session were high. CONCLUSION: Our pilot study suggests that after an intensive 1-week training program, novice investigators with little or no experience in VCP are able to interpret the main capillary abnormalities and SSc pattern and to achieve good interreader agreement rates.
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 | 0.014 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 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".