Evaluation of the Quality of Prognosis Studies in Systematic Reviews
Bibliographic record
Abstract
BACKGROUND: To provide valid assessments of answers to prognostic questions, systematic reviews must appraise the quality of the available evidence. However, no standard quality assessment method is currently available. PURPOSE: To appraise how authors assess the quality of individual studies in systematic reviews about prognosis and to propose recommendations for these quality assessments. DATA SOURCES: English-language publications listed in MEDLINE from 1966 to October 2005 and review of cited references. STUDY SELECTION: 163 systematic reviews about prognosis that included assessments of the quality of studies. DATA EXTRACTION: A total of 882 distinct quality items were extracted from the assessments that were reported in the various reviews. Using an iterative process, 2 independent reviewers grouped the items into 25 domains. The authors then specifically identified domains necessary to assess potential biases of studies and evaluated how often those domains had been addressed in each review. DATA SYNTHESIS: Fourteen of the domains addressed 6 sources of bias related to study participation, study attrition, measurement of prognostic factors, measurement of and controlling for confounding variables, measurement of outcomes, and analysis approaches. Reviews assessed a median of 2 of the 6 potential biases; only 2 (1%) included criteria aimed at appraising all potential sources of bias. Few reviews adequately assessed the impact of confounding (12%), although more than half (59%) appraised the methods used to measure the prognostic factors of interest. LIMITATIONS: Reviews may have been missed by the search or misclassified because of incomplete reporting. Validity and reliability testing of the authors' recommendations are required. CONCLUSIONS: Quality appraisal, a necessary step in systematic reviews, is incomplete in most reviews of prognosis studies. Adequate quality assessment should include judgments about 6 areas of potential study biases. Authors should incorporate these quality assessments into their synthesis of evidence about prognosis.
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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 | 0.705 | 0.897 |
| Meta-epidemiology (narrow) | 0.005 | 0.006 |
| Meta-epidemiology (broad) | 0.023 | 0.027 |
| Bibliometrics | 0.044 | 0.042 |
| Science and technology studies | 0.004 | 0.012 |
| Scholarly communication | 0.019 | 0.016 |
| Open science | 0.009 | 0.009 |
| Research integrity | 0.007 | 0.007 |
| 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".