Validated methods to identify patients with asthma–COPD overlap in healthcare databases: a systematic review protocol
Bibliographic record
Abstract
INTRODUCTION: Asthma-chronic obstructive pulmonary disease (COPD) overlap (ACO) is characterised by patients presenting symptoms of both asthma and COPD. Many efforts have been made to validate different methods of identifying asthma-COPD overlap cases based on symptoms, spirometry and medical history in epidemiological studies using healthcare databases. There are various coding algorithm strategies that can be used and selection depends on targeted validation. The primary objectives of this systematic review are to identify validated methods (or algorithms) that identify patients with ACO from healthcare databases and summarise the reported validity measures of these methods. METHODS: MEDLINE, EMBASE databases and the Web of Science will be systematically searched by using appropriate search strategies that are able to identify studies containing validated codes and algorithms for the diagnosis of ACO in healthcare databases published, in English, before October 2018. For each selected study, we require the presence of at least one test measure (eg, sensitivity, specificity etc). We will also include studies, in which the validated algorithm is compared with an external reference standard such as questionnaires completed by patients or physicians, medical charts review, manual review or an independent second database. For all selected studies, a uniform table will be created to summarise the following vital information: name of author, publication year, country, data source, population, clinical outcome, algorithms, reference standard method of validation and characteristics of the test measure used to determine validity. PROSPERO REGISTRATION NUMBER: CRD42018087472.
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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 | 0.099 | 0.108 |
| Meta-epidemiology (narrow) | 0.005 | 0.006 |
| Meta-epidemiology (broad) | 0.018 | 0.014 |
| Bibliometrics | 0.020 | 0.017 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.007 | 0.010 |
| Open science | 0.006 | 0.006 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.049 | 0.006 |
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".