Chronic rhinosinusitis identification in administrative databases and health surveys: A systematic review
Bibliographic record
Abstract
OBJECTIVES/HYPOTHESIS: Much of the epidemiological data on chronic rhinosinusitis (CRS) are based on large administrative databases and health surveys. The accuracy of CRS identification with these methods is unknown. METHODS: A systematic review was performed to identify studies that measured the accuracy of CRS diagnoses in large administrative databases or within health surveys. The Quality Assessment of Diagnostic Accuracy Studies 2 tool was used to assess study quality. RESULTS: Of 512 abstracts initially identified, 122 were selected for full-text review; only three studies (2.5%) measured the accuracy of CRS patient identification. In a single, large administrative database study with a CRS prevalence of 54.8%, a single International Classification of Diseases-9th Revision diagnostic code for CRS had a positive predictive value (PPV) of only 34%. A diagnostic code algorithm identified CRS patients with a PPV of 91.3% (95% confidence interval [CI], 85.3-95.1); in a population with a CRS prevalence of 5%, this algorithm had a PPV of 31%. In health survey studies having an estimated CRS prevalence of 25% to 46%, self-reported symptom-based CRS diagnosis had a PPV of 62% (95% CI, 50.2-72.1) when nasal endoscopy was the gold standard for CRS diagnosis, and 70% (95% CI, 57.4-80.8) when otolaryngologist-based CRS diagnosis (after interview and nasal endoscopy) was the gold standard. CONCLUSION: Most health administrative data and health surveys examining CRS did not consider the accuracy of case identification. For unselected populations, administrative data and health surveys using self-reported diagnoses inaccurately identify patients with CRS. Epidemiological results based on such data should be interpreted with these results in mind. Laryngoscope, 126:1303-1310, 2016.
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 | 0.025 | 0.130 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.010 | 0.008 |
| Bibliometrics | 0.017 | 0.019 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".