Use of fragmented <scp>QRS</scp> in prognosticating clinical deterioration and mortality in pulmonary embolism: A meta‐analysis
Bibliographic record
Abstract
Background Fragmented QRS ( fQRS ) on electrocardiography is potentially valuable in prognosticating acute pulmonary embolism ( PE ). ECG is one of the first tests performed in the emergency department, quickly interpretable, noninvasive, inexpensive, and available in remote areas. We aimed to review fQRS 's role in PE prognostication. Methods We searched MEDLINE , EMBASE , Google Scholar, Web of Science, abstracts, conference proceedings, and reference lists until October 2017. Eligible studies used fQRS to prognosticate patients for the main outcomes of death and clinical deterioration or escalation of therapy. Two authors independently selected studies, with disagreement resolved by consensus. Ad hoc piloted forms were used to extract data and assess risk of bias. We used a random‐effects model to pool relevant data in meta‐analysis with odds ratios ( OR ) and 95% confidence intervals ( CI ), while all other data were synthesized qualitatively. Statistical heterogeneity was assessed using the I 2 index. Results We included five studies (1,165 patients). There was complete agreement in study selection. fQRS significantly predicted in‐hospital mortality ( OR [95% CI ], 2.92 [1.73–4.91]; p < .001), cardiogenic shock ( OR [95% CI ], 4.71 [1.61–13.70]; p = .005), and total mortality at 2‐year follow‐up ( OR [95% CI ], 4.42 [2.57–7.60]; p < .001). Adjusted analyses were generally consistent with these results. Conclusion Although few studies have explored the current study's question, they showed that fQRS is potentially valuable in PE prognostication. fQRS should be considered as an entry, along with other clinical and ECG findings, in a PE risk score.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.010 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".