Profile of Ovarian Cancer Patients Seeking Information from a Web-Based Decision Support Program
Bibliographic record
Abstract
BACKGROUND: There is limited information available regarding the characteristics of patients who elect to gather and share information about their malignancy on the Internet. METHODS: Using a proprietary decision support program embedded into a number of established websites, individuals entered personal clinical data into disease site profilers designed to provide information about evidence-based treatment options, based on specific characteristics (e.g., stage of disease, prior therapy) provided by the patients. The aggregate data were evaluated to examine the characteristics of patients with gynecological cancer (with a focus on newly diagnosed and recurrent ovarian cancer) using such a tool. RESULTS: From early 2000 through November 2004, >15,000 patients with gynecological cancer have entered data into one of four profilers: newly diagnosed (n = 5604)/recurrent (n = 2803) ovarian, endometrial, and cervical cancers. Internal data consistency includes similar ages and general health histories of the ovarian and endometrial cancer populations and younger age of the cervical cancer patients. Whereas 90% of the women with ovarian cancer considered themselves to be in "good health," 64% of newly diagnosed vs. only 50% of recurrent disease patients declared their activity level was "normal." Of the recurrent patients, 32% stated they had undergone a secondary surgery. The overall aggressive management philosophy of the recurrent patients in this series is supported by the observation that 33% had received > or =4 prior chemotherapy regimens, 97% desired additional treatment, and 81% were interested in clinical trials. CONCLUSIONS: Women with ovarian cancer seeking assistance from web-based decision support programs may represent a subgroup with unique clinical features compared with the general patient population.
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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 | 0.001 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".