Discharge Abstract Database, 2021-2022 and 2023-2024 [Canada]: Geographic Detail File
Bibliographic record
Abstract
The Discharge Abstract Database captures administrative, clinical, and demographic information on hospital discharges. Two files are available: Clinical Detail and Geographic Detail. These files contain a 10% sampling of persons from the database. Reference dates were randomly assigned to selected individuals. The Clinical Detail File includes inpatient data from all acute care institutions in Canada (excluding stillbirths and cadaveric donor cases). Data includes diagnosis, interventions, special care, length of stay, newborn weights, and gestation weeks at delivery. The Geographic Detail File includes inpatient data from all acute care institutions in Canada (excluding stillbirths and cadaveric donor cases). Data includes Health Region, case mix variables, and length of stay. Common data elements in the Clinical Detail and Geographic Detail Files are person identifier, facility province (territories are combined), discharge day, admission day, gender, and age group. You must read and accept the terms of the license agreement before you can obtain the data and documentation. The purpose of the DAD sample files is for the researchers to become familiar with the structure and content of DAD data, as well as to explore relationships among data elements. The sample files give researchers the chance to work with the data and clarify data requirements before making a formal data request to CIHI. They are not meant for completing an actual research project. See also the Discharge Abstract Database, 2021-2022 and 2023-2024: Clinical Detail File: https://borealisdata.ca/dataset.xhtml?persistentId=doi:10.5683/SP3/2UV6TO
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 | 0.001 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.013 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.275 | 0.072 |
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".