Bibliographic record
Abstract
BACKGROUND: Clinicians need an accurate diagnostic test for hand-arm vibration syndrome (HAVS). AIMS: To validate a simple thermometric method to diagnose HAVS-related Raynaud's phenomenon. METHODS: Fifteen workers with photographically confirmed HAVS-related Raynaud's phenomenon were compared to controls without Raynaud's phenomenon and an occupational history of hand-arm vibration exposure. Digit temperatures were measured using an infrared thermometer before and after immersion in 5 degrees C water for 1 min. RESULTS: The HAVS patients differed significantly from the controls in terms of baseline temperature, rewarming time and rate. The fingertip-base temperature gradient was more commonly positive among the controls. CONCLUSIONS: The test method evaluated in this study is simple, cheap and accurate. If the pre-test probability is at least 35%, the best test variable to confirm the diagnosis of HAVS-related Raynaud's phenomenon is the time to rewarm to baseline of the first three fingertips providing the interval is > or =8-9 min.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.001 | 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 teacher head, 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".