Derivation and Validation of a MEDLINE Search Strategy for Research Studies That Use Administrative Data
Bibliographic record
Abstract
OBJECTIVE: To derive and validate a search strategy that identifies administrative database research (ADR) in the MEDLINE database. DESIGN: Analytical survey. METHODS: We downloaded all articles published between January 1, 2008 and October 7, 2009 in 20 top journals in internal medicine, cardiovascular medicine, public health, and health services research. These were reviewed to determine whether they were ADR (in which the study cohort, exposure, or outcome was defined using electronic data created for or during the processing of patients through their health care). We used chi-squared recursive partitioning to create a search strategy that maximized sensitivity based on publication type, MeSH headings, and text words. MAIN OUTCOME MEASURES: Sensitivity and positive predictive value of the search strategy for true ADR in three samples: derivation (n=5,513); internal validation (n=2,710); and external validation (n=1,500). RESULTS: The prevalence of ADR in the derivation, internal validation, and external validation samples was 2.6, 2.9, and 2.2 percent, respectively. The sensitivity of our search strategy in these samples was 90.9 percent (95 percent confidence interval [CI] 85.0-95.1), 88.5 percent (79.2-94.6), and 100 percent (99.3-100), respectively. The positive predictive value in these samples was 10.7 percent (9.0-12.6), 11.5 percent (9.1-14.4), and 3.3 percent (2.3-4.6), respectively. CONCLUSION: We derived and validated a search strategy that is highly sensitive for ADR in MEDLINE.
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
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Methods About the Canadian research system: no · About a Canadian topic: no | Simulation or modeling | low |
| gpt | no category Domain: not available · Genre: Methods About the Canadian research system: no · About a Canadian topic: no | Other design | high |
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 | 0.298 | 0.691 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.007 | 0.008 |
| Bibliometrics | 0.070 | 0.033 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.005 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".