Bibliographic record
Abstract
ContextExperienced clinicians' gestalt is useful in estimating the pretest probability for pulmonary embolism and is complementary to diagnostic testing, such as lung scanning. However, it is unclear whether recently developed clinical prediction rules, using explicit features of clinical examination, are comparable with clinicians' gestalt. If so, clinical prediction rules would be powerful tools because they could be used by less-experienced health care professionals to simplify the diagnosis of pulmonary embolism. Recent studies have shown that the combination of a low pretest probability (using a clinical prediction rule) and a normal result of a D-dimer test reliably excludes pulmonary embolism without the need for further testing.ObjectiveTo evaluate and demonstrate the accuracy of pretest probability assessment for pulmonary embolism using clinical gestalt vs clinical prediction rules.Data SourcesThe MEDLINE database was searched for relevant articles published between 1966 and March 2003. Bibliographies of pertinent articles also were scanned for suitable articles.Study SelectionTo be included in the analysis, studies were required to have consecutive, unselected patients enrolled; participating physicians in the studies, blinded to the results of diagnostic testing, had to estimate pretest probability of pulmonary embolism; and validated diagnostic methods had to be used to confirm or exclude pulmonary embolism.Data ExtractionThree reviewers independently scanned titles and abstracts for inclusion of studies. An initial MEDLINE search identified 1709 studies, of which 16 involving 8306 patients were included in the final analysis.Data SynthesisA clinical gestalt strategy was used in 7 studies, and in the low, moderate, and high pretest categories, the rates of pulmonary embolism ranged from 8% to 19%, 26% to 47%, and 46% to 91%, respectively. Clinical prediction rules were used in 10 studies, and 3% to 28%, 16% to 46%, and 38% to 98% in the low, moderate, and high pretest probability groups, respectively, had pulmonary embolism.ConclusionsThe clinical gestalt of experienced clinicians and the clinical prediction rules used by physicians of varying experience have shown similar accuracy in discriminating among patients who have a low, moderate, or high pretest probability of pulmonary embolism. We advocate the use of a clinical prediction rule because it has shown to be accurate and can be used by less-experienced clinicians.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".