Bibliographic record
Abstract
To the Editor: We thank Dr. Hughes for his interest1 in our article2 reporting factors influencing Raynaud phenomenon (RP) symptom reporting in patients with systemic sclerosis (SSc). We presented data demonstrating differences in Raynaud symptom reporting using the Raynaud Condition Score (RCS) diary depending on season of enrollment2. A weaker-than-expected relationship between external temperature (using Meterological Office data) and contemporaneous collection of the RCS diary (Spearman ρ ∼ −0.25) suggests that other factors beyond cold exposure may contribute to Raynaud burden in SSc2. Dr. Hughes has presented an analysis of the effect of season on Raynaud by evaluating the influence of seasonal variation on Internet searches using the term “Raynaud phenomenon”. Consistent with an anticipated relationship between cold exposure and Raynaud symptoms, a clear pattern emerges highlighting increasing health-seeking Internet search activity for Raynaud during the colder months, with troughs during warmer seasons. An evaluation of mean monthly UK temperatures over the same period of analysis suggests seasonal factors beyond external temperature may contribute to information-seeking behavior for Raynaud symptoms. … Address correspondence to J.D. Pauling, Senior Lecturer and Consultant Rheumatologist, Royal National Hospital for Rheumatic Diseases, Upper Borough Walls, Bath, BA1 1RL, UK. E-mail: JohnPauling{at}nhs.net
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 | 0.004 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.033 | 0.034 |
| Insufficient payload (model declined to judge) | 0.007 | 0.005 |
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".