Bibliographic record
Abstract
CONTEXT: Pleural effusion is a common finding among patients presenting with respiratory symptoms. The value of the bedside examination to detect pleural effusion is unclear. OBJECTIVE: To systematically review the evidence regarding the accuracy of the physical examination in assessing the probability of a pleural effusion. DATA SOURCES: We searched MEDLINE (1950-October 2008) and EMBASE (1980-October 2008) using Ovid to identify English-language studies conducted in a clinical setting. Additional studies were identified by searching the bibliographies of retrieved articles and contacting experts in the field. STUDY SELECTION: We included prospective studies of diagnostic accuracy that compared at least 1 physical examination maneuver with radiographic confirmation of pleural effusion. DATA EXTRACTION: Three authors independently appraised study quality and extracted relevant data. Data regarding participant recruitment, reference standard, diagnostic test(s), and test accuracy were extracted. Disagreements were resolved by consensus. DATA SYNTHESIS: We identified 310 unique citations, but only 5 prospectively conducted studies met inclusion criteria (N = 934 patients). A random-effects model was used for quantitative synthesis. Of the 8 physical examination maneuvers evaluated in the included studies (conventional percussion, auscultatory percussion, breath sounds, chest expansion, tactile vocal fremitus, vocal resonance, crackles, and pleural friction rub), dullness to conventional percussion was most accurate for diagnosing pleural effusion (summary positive likelihood ratio, 8.7; 95% confidence interval, 2.2-33.8), while the absence of reduced tactile vocal fremitus made pleural effusion less likely (negative likelihood ratio, 0.21; 95% confidence interval, 0.12-0.37). CONCLUSIONS: Based on the limited number of studies, dullness to percussion and tactile fremitus are the most useful findings for pleural effusion. Dull chest percussion makes the probability of a pleural effusion much more likely but requires a chest radiograph to confirm the diagnosis. When the pretest probability of pleural effusion is low, the absence of reduced tactile vocal fremitus makes pleural effusion less likely so that a chest radiograph might not be necessary depending on the overall clinical situation.
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 | 0.005 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.005 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".