Proactive assessment of patient reported outcomes in patients with ovarian cancer.
Bibliographic record
Abstract
5567 Background: Patient reported outcome (PRO) measures are key instruments to provide an evaluation of health outcomes from the patient's perspective. Assessment of PROs may help identify the nature, severity, and time course of symptoms of concern in patients with ovarian cancer. The ability to access this information in real-time, including changes over time, could improve patient safety and decision-making. Methods: This systematic review evaluated the proactive or real-time assessment of PROs in patients with ovarian cancer undergoing systemic therapy. Medline, Embase, and Cochrane databases were searched (up to February 2022), and prospective ovarian cancer studies (experimental or observational) that incorporated PROs (including quality of life) were included. Conference abstracts were excluded. Primary objective was to assess the frequency of studies incorporating proactive use of PROs. A secondary objective was to describe PRO reporting. Descriptive statistics were used. Results: 3,071 articles were screened, with 117 included in the final analysis. Studies were published between 1990-2022 and contained 35,735 patients (median 140 patients per study; inter-quartile range 58-415). Median time from patient enrollment initiation to study publication was 7 years (range 1-15). Most studies were experimental/clinical trials (n=93, 79%), followed by observational (n=23, 20%) and not reported (n=1; Table). Among experimental studies, 56% (52/93) were phase III, 35.5% (33/93) phase II, 6.5% (6/93) phase I or I/II trials and it was not reported in two. Therapeutic strategies were assessed in 98% (91/93) of experimental studies, being the most frequent one chemotherapy (n=53, 58%), followed by antiangiogenics and PARP inhibitors (n=8, 9%, each). Types of observational studies were descriptive 43.5% (10/23), cohort 26% (6/23), cross-sectional 22% (5/23), or other 9% (2/23). PROs were the primary objective in 7.5% (7/93) and 83% (19/23) of experimental and observational studies respectively. The table describes PRO reporting standards per type of study. PROs were assessed in real-time in 0.8% (1/117) of studies. Conclusions: Completion of PRO and quality of life questionnaires involve time and effort for patients with ovarian cancer. PRO questionnaire responses were only assessed in real-time in <1% of analyzed studies. Efforts should be made to incorporate proactive assessment of PROs to optimize patient care and safety. [Table: see text]
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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 | 0.022 | 0.085 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.007 | 0.010 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".