Academic Productivity of Interventional Pulmonology Training Programs
Bibliographic record
Abstract
RATIONALE: The Hirsch index (h-index) has been validated as a measure of academic productivity and may be an appropriate tool to assess the scholarly activity of interventional pulmonology (IP). OBJECTIVES: This study aimed to elucidate the factors associated with increasing h-index scores among IP training programs. METHODS: A cross-sectional study was conducted of IP training programs across the United States and Canada. Data, including their respective h-index, number of publications, academic rank, geographic location, and possession of an advanced degree, were collected on IP faculty and fellows from 23 teaching institutions. MEASUREMENTS AND MAIN RESULTS: Ninety-three IP physicians (48 faculty, 45 fellows) in all were included in the study from 23 institutions with a total of 101 data points. The faculty h-index mean was 3.88. The proportion of faculty with an h-index greater than the mean value was increased significantly with higher academic rank (P < 0.0001). In addition, physicians holding an advanced degree beyond an M.D./D.O. had a significantly higher h-index than did those without (P = 0.0062). CONCLUSIONS: For academic interventional pulmonologists, the h-index rises with increasing academic rank and possession of an advanced degree. The h-index for IP is roughly comparable to that for other surgical and procedural-based specialties.
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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 | 0.002 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".