Same Question, Different Data Source, Different Answers? Data Source Agreement for Surgical Procedures on Women with Breast Cancer
Bibliographic record
Abstract
This study assessed the accuracy of the Manitoba Cancer Registry (MCR) and two administrative data sources, the Manitoba Health hospital discharge file and the Manitoba Health medical claims file, for capturing surgical procedures related to the treatment of breast cancer. The study cohort included all women diagnosed in Manitoba with invasive or in situ breast cancer between 1995 and 1999. The surgical procedures of interest were mastectomy, breast conserving surgery and axillary node dissection. Analysis focused on assessing concordance between data sources following record linkage. Agreement was measured using the kappa statistic, and chart reviews of discordant information were completed to identify the more reliable data source and to validate data files. The effect of using each data set alone to calculate procedure rates was determined to identify any clinically important differences arising from the choice of data source. Results indicate that capture of breast cancer patients using administrative data sets alone can be quite good and that the population-based cancer registry is superior to other administrative data sets for capturing surgical treatment information on cancer cases.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".