Comparison and Impact of Four Different Methodologies for Identification of Ambulatory Care Sensitive Conditions
Bibliographic record
Abstract
Ambulatory care sensitive conditions (ACSCs) are conditions for which hospitalizations are thought to be avoidable if effective and accessible primary health care is available. However, to define which conditions are considered ACSCs, there is a considerable number of different lists. Our aim was to compare the impact of using different ACSC lists considering mainland Portugal hospitalizations. A retrospective study with inpatient data from Portuguese public hospital discharges between 2011 and 2015 was conducted. Four ACSC list sources were considered: Agency for Healthcare Research and Quality (AHRQ), Canadian Institute for Health Information (CIHI), the Victorian Ambulatory Care Sensitive Conditions study, and Sarmento et al. Age-sex-adjusted rates of ACSCs were calculated by district (hospitalizations per 100,000 inhabitants). Spearman's rho, the intraclass correlation coefficient (ICC), the information-based measure of disagreement (IBMD), and Bland and Altman plots were computed. Results showed that by applying the four lists, different age-sex-adjusted rates are obtained. However, the lists that seemed to demonstrate greater agreement and consistency were the list proposed by Sarmento et al. compared to AHRQ and the AHRQ method compared to the Victorian list. It is important to state that we should compare comparable indicators and ACSC lists cannot be used interchangeably.
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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 | 0.111 | 0.180 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".