Catamenial pneumothorax: A population-based study
Bibliographic record
Abstract
Study objective: To characterize the incidence, clinical features, and demographic characteristics of women with catamenial pneumothorax with the use of a large national database in the United States. Design: Retrospective case control study of the Healthcare Cost and Utilization Project, Nationwide Inpatient Sample (HCUP-NIS) database between 2004 and 2014. Setting: Not applicable. Patients: Women, aged 18–50 years, who were diagnosed with a pneumothorax between 2004 and 2014. Intervention: Not applicable. Measurements: The cohort was constructed using the appropriate ICD-9 codes utilizing HCUP-NIS, checking for women with spontaneous and catamenial pneumothorax . Main results: Of a total 6234 women with spontaneous pneumothorax, 80 women (1.3%) had a diagnosis of catamenial pneumothorax. Women with catamenial pneumothorax were more likely to be black, have private insurance and be treated in an urban hospital. In addition, fewer patients in this group reported a history of tobacco consumption. Patients in the study group were more likely to have concomitant pelvic endometriosis, dysmenorrhea, and infertility. Compared to women with non-catamenial pneumothorax, those with catamenial pneumothorax were more frequently treated with thoracic drainage and underwent pleural and diaphragm biopsies. Conclusion: Catamenial pneumothorax is a rare entity with a distinct clinical profile. This condition is associated with pelvic endometriosis, has a predilection for surgical intervention and is associated with lower rates of smoking.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".