Explanation of the problem of pulmonary embolism in relation to the optimization of modern algorithms for the actions of the family doctor
Bibliographic record
Abstract
The purpose of the work is to ensure the improvement of the quality of education and educational services at a level that meets the expectations and needs of the family doctor, contributes to the optimization of the educational process on the problem of pulmonary embolism. Determination of clinical probability is an important component of management of patients with suspected PE. In clinical practice, the Canadian (P.S. Wells) and Geneva scales are most often used for this. For a patient with a high clinical probability of PE, multispiral computer tomography (MSCT) is of primary importance. With suspected high-risk PE, evidenced by shock or hypotension, CT angiography or bedside transthoracic echocardiography should be performed for diagnostic purposes. Today, magnetic resonance imaging is not recommended for the diagnosis of PE. Outpatient treatment should be carried out with PESI class I-II. Inpatient treatment - with PESI III-V class. Anticoagulant therapy (ACT), which should be started as early as possible at the stage of diagnosis, is the basis of VE treatment. Thrombolytic therapy should be carried out in the clinic of shock or in the presence of hemodynamic instability. As a basis for planning the educational process, it is desirable to take the constructive alignment model, which consists of the following three logically interconnected components. First of all, these are learning outcomes that must be aligned with the goals of the curriculum. Secondly, educational activities should be related to expected learning outcomes. An important component is assessment and feedback. The professional training of a general practitioner - a family doctor is aimed at acquiring new knowledge, deepening professional competences and improving practical skills to maintain an appropriate level of training for today's urgent problem - pulmonary embolism, taking into account the realities of wartime and the threat of repeated outbreaks of covid infection (CI)
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 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".