My Patient is Short of Breath: Is the Problem in the Lung Tissue?
Bibliographic record
Abstract
Undifferentiated dyspnoea is a common patient presentation in the intensive care unit, medical and surgical floors, and in the emergency department. Physical examination and chest radiography are notoriously insensitive for detection and differentiation of various lung pathologies while computed tomography consumes significant resources and exposes the patient to ionizing radiation. Point-of-care ultrasound (PoCUS) is a highly sensitive and specific diagnostic tool that, with appropriate operator experience, is capable of diagnosing and differentiating between the various causes of dyspnoea. PoCUS machines are readily available, images are rapidly generated and repeatable, and technical skills are easily taught during short training sessions. Furthermore, the development of PoCUS skills in one specific area enables and enhances the development of skills in other non-related areas. This article describes the benefits, technical aspects, and challenges associated with using PoCUS to examine the lung parenchyma in the acutely dyspnoeic patient.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".