Implementation of a new patient health questionnaire into standard practice in outpatient cancer clinics to improve patient care and quality of treatment.
Bibliographic record
Abstract
214 Background: Patient socio-demographic, lifestyle, and risk factor information at the Princess Margaret Cancer Centre (PM) is routinely collected for clinical purposes. The only standardized patient information presently being gathered in the outpatient cancer clinics at the PM is symptom management data, which is linked directly into the electronic medical records. Collecting and recording additional data can improve the quality of patient care, help identify risk factors, and guide treatment options. Our aim was to determine the feasibility of collecting this additional information in a clinical setting. Methods: This pilot cohort study was implemented in the thoracic outpatient oncology clinic at the PM. It involved developing a questionnaire utilizing literature sources, expert review, and pilot testing. Adult cancer patients completed the questionnaire and a complementary acceptability survey during their first clinic visit. Results: 170 patients with thoracic tumours, primarily lung cancer, took part in the feasibility study. Of these, 51% were female, 67% were Caucasian, and the median age was 65 (range 32 to 88) years old. The acceptability survey demonstrated that: 76% of respondents found that the questionnaire did not make their clinic visit more difficult, 68% found that it asked the right questions, 79% thought the questionnaire contained pertinent information for their doctor and other healthcare providers to know, and 51% found that it was time consuming to complete. Conclusions: This study determined that it is feasible to implement a standardized questionnaire that gathers patient socio-demographic, lifestyle, and risk factor information in routine clinical cancer care. Since half of the study population found the questionnaire time consuming to complete it should be administered prior to patient visits, in an electronic format, and with greater explanation/education. The next phase is converting the questionnaire into an electronic version, which aligns with the preferences of study participants and will allow the information to be more easily accessible by clinicians/researchers.
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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 | 0.037 | 0.040 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".