Primary Health Care Appointments and Hospital Stay: An Impact Analysis
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Bibliographic record
Abstract
The study of avoidable hospitalizations has gained international prominence due to its potential to assess the performance of healthcare systems. In Canada and Spain, these hospitalizations are analyzed through Ambulatory Care Sensitive Conditions (ACSC), indicating situations that could have been pre-vented or treated without hospitalization. In Portugal, this concept is represented by the term ICSCSP, focusing on care provided in Primary Health Care (PHC). The data analysis in this study aims to determine the impact that medical appointments at PHC may have on the number of hospitalizations, namely, to determine whether where the number of medical appointments is greater, the number of hospital stays is lower. During the COVID-19 pandemic in Portugal, many hospitalizations of the elderly were due to the decompensation of chronic diseases, highlighting the importance of access to PHC during health emergencies. Data pre-processing was carried out using the Pandas library in Python, merging two datasets monitoring the evolution of hospitalizations and medical appointments in PHC. Despite some challenges encountered during the analysis, such as population bias in district comparisons and the need to adjust metrics to properly reflect the relationship between appointments and hospital stays, it was concluded that the number of appointments in PHC does not have a direct impact on hospitalizations. For a more accurate analysis, it would be necessary to consider other factors, such as patient and district characteristics, and conduct more targeted studies, especially after disruptive events like the COVID-19 pandemic. This more detailed analysis would allow for a better understanding of the relationship between medical appointments and hospitalizations, contributing to improvements in the healthcare system.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 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 it