Role of patient‐centred outcomes after hospital discharge: a state‐of‐the‐art review
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
The traditional approach for measuring outcomes after surgery involves ascertaining whether a patient survived surgery while avoiding major complications. This approach does not capture the full spectrum of events that are meaningful to patients, especially because mortality risks after elective surgery are relatively low, and different complication types vary considerably with respect to their impact on postoperative recovery. This review discusses the application, advantages, disadvantages and select examples of patient-centred outcomes in peri-operative medicine. When applied appropriately, these outcomes complement traditional clinical outcomes, identify important changes in postoperative function that impact patients without discernible complications and ensure that the definition of success after surgery is more meaningful to all relevant stakeholders.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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