Functional testing for the detection of restenosis after percutaneous transluminal coronary angioplasty: a meta-analysis.
Bibliographic record
Abstract
BACKGROUND: A number of studies have examined the ability of functional testing to detect restenosis after percutaneous transluminal coronary angioplasty (PTCA). However, a meta-analysis of these studies has not been performed. OBJECTIVES: To pool the results of studies examining the diagnostic abilities of exercise treadmill testing (ETT), stress nuclear imaging and stress echocardiographic imaging at six months to detect post-PTCA restenosis. The secondary objective was to examine, through the use of a theoretical model, the impact of stenting on the yield of post-PTCA functional testing. PATIENTS AND METHODS: A MEDLINE search was conducted to identify studies examining post-PTCA functional testing for the diagnosis of restenosis. The English-language literature was examined for the years 1975 to 2000. Appropriate articles were identified, and their references were examined to identify additional studies. The sensitivities and specificities of these studies were then pooled and Bayes' theorem was used to examine the effect of stenting on the diagnostic abilities of post-PTCA functional testing. RESULTS: A pooled analysis showed that ETT alone has a poor sensitivity (46%, 95% CI 33% to 58%) and a moderate specificity (77%, 95% CI 67% to 86%) for the identification of post-PTCA restenosis. The use of nuclear imaging increases the sensitivity (87%, 95% CI 74% to 100%) and the specificity (78%, 95% CI 74% to 81%). Echocardiographic imaging also increases both sensitivity (63%, 95% CI 15% to 100%) and specificity (87%, 95% CI 72% to 100%). The positive likelihood ratios for ETT alone, nuclear imaging and echocardiographic imaging were calculated to be 1.94, 3.93 and 4.94, respectively. Conversely, the negative likelihood ratios were calculated to be 0.71, 0.16 and 0.43, respectively. As restenosis rates decline from 30% to 10%, the false positive rate of stress imaging increases from 37% to 77%. CONCLUSIONS: ETT alone is poorly diagnostic of post-PTCA restenosis, while stress nuclear and stress echocardiographic imaging perform better. However, the value of routine post-PTCA functional testing to detect restenosis is declining because restenosis rates are decreasing.
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 | 0.026 | 0.046 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.053 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".