360 Health Analysis (H360)—A Comparison of Key Performance Indicators in Breast Cancer Management across Health Institution Settings in Portugal
Bibliographic record
Abstract
BACKGROUND: The increased focus on quality indicators (QIs) and the use of clinical registries in real-world cancer studies have increased compliance with therapeutic standards and patient survival. The European Society of Breast Cancer Specialists (EUSOMA) established QIs to assess compliance with current standards in breast cancer care. METHODS: This retrospective study is part of H360 Health Analysis and aims to describe compliance with EUSOMA QIs in breast cancer management in different hospital settings (public vs. private; general hospitals vs. oncology centers). A set of key performance indicators (KPIs) was selected based on EUSOMA and previously identified QIs. Secondary data were retrieved from patients' clinical records. Compliance with target KPIs in different disease stages was compared with minimum and target EUSOMA standards. RESULTS: A total of 259 patient records were assessed. In stages I, II, and III, 18 KPIs met target EUSOMA standards, 5 met minimum standards, and 8 failed to meet minimum standards. Compliance with KPIs varied according to the type of hospital (particularly regarding diagnosis) and disease stage. Although small differences were found in KPI compliance among institutions, several statistical differences were found among treatment KPIs according to disease stage, particularly in stage III. CONCLUSIONS: This study represents the first assessment of the quality of breast cancer care in different hospital settings in Portugal and shows that, although most QIs meet EUSOMA standards, there is room for improvement. Differences have been found across institutions, particularly between oncology centers and general hospitals, in diagnosis and compliance with KPIs among disease stages. Stage III showed the greatest variability in compliance with treatment KPIs, probably related to the lower specificity of the guidelines in this disease stage.
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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 | 0.007 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| 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".