Bibliographic record
Abstract
Point of Care Ultrasound (POCUS) is an ever-evolving technology that has become integral to clinical practice in a variety of domains.Since its inception in Emergency Medicine (EM) in the 1980s, POCUS has traditionally been viewed as a tool to make binary decisions in response to focused clinical questions [1,2].Current literature continues to cite POCUS as a means to rule in or out specific diagnoses and answer simple, yes-or-no questions: Is there a live intrauterine pregnancy?Is this abdominal pain caused by an abdominal aortic aneurysm?Is this leg swelling due to a deep vein thrombosis [3-5]?The adage that each POCUS scan should answer one specific question is often recited in POCUS education tools, and POCUS archiving and reporting materials are typically centered around this same binary paradigm [6].This view is particularly prevalent among those who are less familiar with POCUS, who view the tool as a limited, operatordependent technology to make these types of quick decisions.
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 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 | 0.021 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.008 |
| Scholarly communication | 0.007 | 0.011 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.007 | 0.010 |
| Insufficient payload (model declined to judge) | 0.011 | 0.004 |
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".