Development of a PREM for acute leukemia and/or stem cell transplantation.
Bibliographic record
Abstract
195 Background: People with complex hematologic malignancies (eg. acute leukemia) require highly specialized care from the time of initial diagnosis, throughout treatment and into survivorship. Often, stem cell transplantation is an essential component of treatment for some people with complex hematologic malignancies. A challenge with generic patient-reported experience measure (PREM) tools is that they do not capture areas of concern for a specific population, which was the case for people with complex hematologic malignancies. Therefore, a disease-specific PREM was developed to address relevant aspects of the patient experience. Methods: The development consisted of:1)literature review to identify relevant patient experience items;2)modified Delphi process where patient experience items were rated by experts (n = 10); 3)cognitive interviews (n = 8) to ensure that items were clear and understandable; and 4)pilot of survey. Results: Through the review of literature, two existing PREMs were identified as relevant for this patient population and potential items were extracted:1)the Ambulatory Oncology Patient Satisfaction Survey (n = 90); and2)the Canadian Patient Experience Survey – Inpatient Care (n = 44).The modified Delphi processes consisted of three rounds (an online survey and two in-person meetings to discuss results), which resulted in 73 items being eliminated and 29 items being added.A 90-item PREM was utilized for the cognitive interviews with patients and/or family members, where 14 items were removed and 6 items were added. Conclusions: The PREM was named Your Voice Matters-Acute Leukemia and Stem Cell Transplant, and includes 82 items for measuring the patient experience of complex hematologic malignancies.Surveys were distributed via mail to eligible patients from 10 Regional Cancer Centres in Ontario, Canada in May 2018.Results are anticipated for August 2018.
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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 | 0.034 | 0.055 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".