Codifying healthcare – big data and the issue of misclassification
Bibliographic record
Abstract
The rise of electronic medical records has led to a proliferation of large observational studies that examine the perioperative period. In contrast to randomized controlled trials, these studies have the ability to provide quick, cheap and easily obtainable information on a variety of patients and are reflective of everyday clinical practice. However, it is important to note that the data used in these studies are often generated for billing or documentation purposes such as insurance claims or the electronic anesthetic record. The reliance on codes to define diagnoses in these studies may lead to false inferences or conclusions. Researchers should specify the code assignment process and be aware of potential error sources when undertaking studies using secondary data sources. While misclassification may be a short-coming of using large databases, it does not prevent their use in conducting meaningful effectiveness research that has direct consequences on medical decision making.
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 | 0.170 | 0.470 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.005 | 0.009 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.015 | 0.015 |
| Insufficient payload (model declined to judge) | 0.004 | 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, 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".