Bias and uncertainty in comparative diagnostic accuracy research
Bibliographic record
Abstract
Healthcare professionals frequently ask how the accuracy of one diagnostic test compares with that of another. To compare the accuracy of tests, studies that directly compare two or more tests are performed, called comparative accuracy studies. These studies can be designed with shortcomings that render the results at risk of being biased. Guidance on how to identify these shortcomings and assess risk of bias can help those performing systematic reviews and those designing comparative accuracy studies. This thesis describes the development a tool for assessing the risk of bias in comparative accuracy studies, named QUADAS-C (Quality Assessment of Diagnostic Accuracy Studies – Comparative). The thesis also describes preparatory work that informed the development of this tool, a guidance on how to assess the overall certainty of a body of evidence about comparative accuracy, and an application of the developed methods in a systematic review about point-of-care tests for tuberculosis disease.
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 | 0.641 | 0.876 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.011 | 0.011 |
| Bibliometrics | 0.022 | 0.019 |
| Science and technology studies | 0.004 | 0.020 |
| Scholarly communication | 0.015 | 0.015 |
| Open science | 0.007 | 0.013 |
| Research integrity | 0.010 | 0.009 |
| Insufficient payload (model declined to judge) | 0.005 | 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".