Ruling out pulmonary embolism across different subgroups of patients and healthcare settings: protocol for a systematic review and individual patient data meta-analysis (IPDMA)
Bibliographic record
Abstract
BACKGROUND: Diagnosing pulmonary embolism in suspected patients is notoriously difficult as signs and symptoms are non-specific. Different diagnostic strategies have been developed, usually combining clinical probability assessment with D-dimer testing. However, their predictive performance differs across different healthcare settings, patient subgroups, and clinical presentation, which are currently not accounted for in the available diagnostic approaches. METHODS: This is a protocol for a large diagnostic individual patient data meta-analysis (IPDMA) of currently available diagnostic studies in the field of pulmonary embolism. We searched MEDLINE (search date January 1, 1995, till August 25, 2016) to retrieve all primary diagnostic studies that had evaluated diagnostic strategies for pulmonary embolism. Two authors independently screened titles, abstracts, and subsequently full-text articles for eligibility from 3145 individual studies. A total of 40 studies were deemed eligible for inclusion into our IPDMA set, and principal investigators from these studies were invited to participate in a meeting at the 2017 conference from the International Society on Thrombosis and Haemostasis. All authors agreed on data sharing and participation into this project. The process of data collection of available datasets as well as potential identification of additional new datasets based upon personal contacts and an updated search will be finalized early 2018. The aim is to evaluate diagnostic strategies across three research domains: (i) the optimal diagnostic approach for different healthcare settings, (ii) influence of comorbidity on the predictive performance of each diagnostic strategy, and (iii) optimize and tailor the efficiency and safety of ruling out PE across a broad spectrum of patients with a new, patient-tailored clinical decision model that combines clinical items with quantitative D-dimer testing. DISCUSSION: This pre-planned individual patient data meta-analysis aims to contribute in resolving remaining diagnostic challenges of time-efficient diagnosis of pulmonary embolism by tailoring available diagnostic strategies for different healthcare settings and comorbidity. SYSTEMATIC REVIEW REGISTRATION: Prospero trial registration: ID 89366.
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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 | 0.072 | 0.145 |
| Meta-epidemiology (narrow) | 0.006 | 0.004 |
| Meta-epidemiology (broad) | 0.018 | 0.028 |
| Bibliometrics | 0.012 | 0.011 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.042 | 0.004 |
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".