Utilidad de los modelos clínicos en la predicción de tromboembolia pulmonar
Bibliographic record
Abstract
OBJECTIVE: We considered to evaluate the efectivity of the clinical models for predicting pulmonary thromboembolism (PE). METHODS: Retrospective application of three published clinical models (Wells or Canadian model, Geneva model and Pisa model) to patients unequivocally diagnosed of acute PE. RESULTS: We evaluate 120 patients [Mean age 71+/-13 years, males 63 (52%)]: Nineteen (16%) diagnosed with pulmonary arteriography and 101 (84%) diagnosed with helical computed tomography. In the Canadian model 24% patients were of high clinical probability, 59% intermediate and 17% low clinical probability. In Geneva model 21% patients belonged to high clinical probability, 69% intermediate and 10% low clinical probability. In Pisa model 49% patients were of high clinical probability, 45% intermediate and 6% of low clinical probability. Sensitivity was 0.59, 0.67 and 0.89 respectively. Factors associated with low probability were in Canadian model the heart rate, the absence of signs of deep venous thrombosis, the presence of an alternative diagnosis and the low rate of cancer. In Geneva model, age, normal heart rate and PaO2 higher 70 mm Hg were associated with low probability, while in Pisa model normal chest X-Ray and radiological signs of pulmonary oedema were also associated with low clinical probability. CONCLUSIONS: Although all three clinical model showed deficiencies Pisa model was the most suitable clinical model for predicting PE. An intermediate clinical probability in the three models, should not serve to rule out PE, besides it is remarkable that patients with low clinical probability still could have PE, providing for clinical models with a limited value.
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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 | 0.007 | 0.034 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".