Bibliographic record
Abstract
Dear Readers, We are thrilled to bring you the second issue of the ninth volume of POCUS Journal. Published since 2016, POCUS Journal is the only multi-disciplinary, peer-reviewed, POCUS-focused journal that is free for authors and readers alike. This issue features a variety of POCUS articles from around the world: from North America, South America, Europe, Asia, and even Australia. In addition to novel cases with striking POCUS images and video clips, this issue boasts a great deal of educational research at all levels: Danila et al. (page 80) report on their study of how POCUS can augment a medical student preclinical cardiovascular course while Piro et al. (page 93) studied the impact of just-in-time POCUS curriculum on internal medicine residents; Janjigian et al. (page 109) studied the longitudinal impact of a faculty POCUS training course. POCUS is a powerful tool with a wide range of applications: for example, it can be used both to diagnose musculoskeletal conditions (Doblinger et al. page 15 and Mack et al. page 27) and to treat musculoskeletal conditions (Bowling et al page 12, and McCreary et al. page 30). POCUS can also be used to predict heart failure readmission (Malagón et al. page 125). We are so excited to showcase pivotal POCUS scholarship like this in the November issue. I hope you enjoy this issue of POCUS Journal as much as I have. A huge thank you to our authors, editorial board, and publisher. Sincerely , Benjamin T. Galen, MD Department of Medicine, Albert Einstein College of Medicine and Montefiore Medical Center, Bronx, NY, USA
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.652 | 0.616 |
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".