Identifying Potentially Avoidable Hospital Admissions From Canadian Long-Term Care Facilities
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND: The provision of preventive services and continuity of care are important aspects of long-term care (LTC). A proposed quality indicator of such care is the rate of hospitalizations due to ambulatory care sensitive conditions (ACSCs). As the ACSC approach to identifying potentially avoidable hospitalizations (PAH) was developed for younger community-dwelling adults in the United States, we sought to examine its applicability as a quality indicator for older institutionalized residents in Canada. METHODS: ACSCs were identified in a linked hospital-based LTC and acute care administrative database at the Institute for Clinical Evaluative Sciences in Ontario, Canada. An expert panel was then convened to assess the applicability of existing ACSCs to an older institutionalized population in Canada and to develop consensus-based revisions appropriate to this setting. The revised definition of PAH was then applied to the same linked database. RESULTS: The proportion of hospitalizations categorized as a PAH using the original ACSCs was 47% (4177 of 8885). The panel suggested the inclusion of 2 new conditions (septicemia and falls/fractures) coupled with the deletion of 4 of the original ACSCs (immunization-preventable conditions; nutritional deficiency; severe ear, nose and throat infections; tuberculosis) that were rare hospital diagnoses in this population. Using the revised definition, 55% of hospitalizations (4874) were identified as potentially avoidable. CONCLUSIONS: Changes to the original list of ACSCs led to more hospitalizations being categorized as potentially avoidable. Significant variation between LTC facilities and over time in our PAH indicator may identify areas for improvement in preventive services and continuity of care for LTC residents.
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.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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 it