Pseudoaneurysm of the brachial artery in a patient who uses intravenous drugs
Bibliographic record
Abstract
<h3>Background</h3> There was no nationwide epidemiological study of Lambert-Eaton myasthenic syndrome (LEMS) in Japan; therefore, we conducted a nationwide survey. <h3>Methods</h3> For the first survey, we sent survey sheets to randomly selected medical departments (n=7545) to obtain the number of LEMS who visited medical departments between 1 January 2017 and 31 December 2017. For the second survey, we sent survey sheets to the corresponding medical departments to obtain clinical information on LEMS. <h3>Results</h3> We received 2708 responses (recovery rate: 35.9%) to the first survey. We estimated the number of LEMS as 348 (95% CI 247 to 449). The prevalence was 2.7 (95% CI 1.9 to 3.5) in 1 000 000 population. As a result of the second survey, we obtained 30 case records of 16 men and 14 women. Fourteen patients (46.7%) had a tumour, and 10 out of 14 tumours were small-cell lung carcinoma (71.4%). There was a predominance of men in the LEMS with tumour (paraneoplastic LEMS, P-LEMS) (n=11, 78.6%) and women in the LEMS without tumour (a primary autoimmune form of LEMS, AI-LEMS) (n=11, 68.8%) (p=0.0136). The onset age (mean (SD)) for the P-LEMS was 67.1 (9.0), and that for AI-LEMS was 57.8 (11.2) years old (p=0.0103). The disease duration (median) for P-LEMS was 2 years, and for AI-LEMS was 7.5 years (p=0.0134). <h3>Conclusions</h3> The prevalence of LEMS in Japan is similar to that in other countries. There are predominances of men in P-LEMS and women in AI-LEMS.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".