A framework to assess patient-reported adverse outcomes arising during hospitalization
Bibliographic record
Abstract
BACKGROUND: The assessment of adverse events from a patient-centered view includes patient-reported adverse outcomes. An adverse outcome refers to any suboptimal outcome experienced by the patient; when adverse outcomes are identified through a patient interview these are called patient-reported adverse outcomes. An adverse event is an adverse outcome that is more likely due to the processes of medical care rather than to the mere progression of disease. In the context of a large-scale study assessing post-hospitalization adverse events, we developed a conceptual framework to assess patient-reported adverse outcomes (PRAOs). This methodological manuscript describes this conceptual framework. METHODS: The PRAO framework builds on a validated adverse event ascertainment method including three phases: Phase 1 involves an inquiry to ascertain the occurrence of any patient-reported adverse outcome. It is completed by a structured telephone interview to obtain details - from a patient perspective - on symptoms that developed and/or worsened after hospitalization. Phase 2 involves the classification of PRAOs by physicians not involved in the patient care. Physician-reviewers then rate the PRAOs using well-adopted scales to determine whether the occurrence was the natural progression of the underlying illness or due to medical care. When the PRAO is rated as "due to medical care", it is then classified as an "adverse event". Phase 3 involves the classification of adverse events as preventable or ameliorable. RESULTS: Out of the 1347 patients contacted at 1-month post-discharge, 469 reported AOs and after reviewing 369 cases, 29 were classified as AEs. Observed agreement levels between raters were 87.3, 85.5, and 85.2 % respectively displaying a good agreement (k > 0.60). CONCLUSION: The framework incorporates PRAOs as a way to identify cases that need to be evaluated for adverse events. Further validation of this framework is warrant with the final aim of implementation at larger scale. The implementation of this framework will enable clinicians, researchers and healthcare institutions to compare outcome rates across providers and over time.
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.
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 | 0.067 | 0.086 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.005 |
| Bibliometrics | 0.014 | 0.007 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".