Identifying Ontario geographic regions to assess adults who present to hospital with laboratory-defined conditions: a descriptive study
Bibliographic record
Abstract
BACKGROUND: In 2007, an electronic repository called the Ontario Laboratories Information System (OLIS) was introduced to allow health care providers timely access to laboratory test results. Since not all laboratories began submitting their data to OLIS simultaneously, we sought to create a date-dependent table of geographic regions (forward sortation areas [FSAs]) from which people would likely present to a hospital linked to OLIS. METHODS: In this descriptive study, we used administrative data to capture adults in Ontario who presented to the emergency department for any reason from 2007 to 2017. To assess changes over time, we classified all emergency department visits into fiscal quarters. The primary outcome measure was the proportion of people in a given FSA presenting to an emergency department at an OLIS-linked hospital (v. a hospital not linked to OLIS). To be included in the catchment area, at least 90% of all emergency department visits in a given quarter from a given FSA must have occurred at an OLIS-linked hospital. RESULTS: By Dec. 31, 2017, 323 (61.4%) of 526 Ontario FSAs were in the catchment area (a population of about 8.5 million). There were no differences in selected demographic characteristics or comorbidities between people residing within the catchment area of OLIS-linked hospitals and those residing in the catchment area of unlinked hospitals on Dec. 31, 2017. We used the FSA information to construct a date-dependent table of geographic areas likely to have hospital laboratory data available in OLIS for future studies. INTERPRETATION: We identified relevant Ontario geographic regions from which people would likely present to a hospital linked to OLIS. These geographic regions constitute a catchment area that may be used in future studies to capture adults who present to an OLIS-linked hospital with laboratory-defined conditions such as acute kidney injury, hyperkalemia and hyponatremia.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".