Reducing the burden of patient reporting of physical function in the chronic care of cancer survivors through a branching logic electronic symptom survey (BLESS).
Bibliographic record
Abstract
5 Background: A patient’s functional status is a key outcome variable used to both measure and improve quality of care in cancer survivors. However, comprehensive research tools such as the HAQ-DI and WHODAS instruments pose 35 questions altogether, increasing patient reporting burden. We evaluated whether a BLESS could be developed with high sensitivity of screening questions followed by questions relevant to specific physical function domain, through a branching logic algorithm. Methods: Adult cancer clinic outpatients at Princess Margaret Cancer Centre used tablet technology to complete the HAQ-DI, WHODAS, EQ-5D-3L and PRO-ECOG. BLESS was developed as an algorithm that used PRO-ECOG/EQ-5D-3L to screen for appropriate domains of HAQ-DI/WHODAS to query. Sensitivity/specificity of BLESS screeners to HAQ-DI/WHODAS items were reported. BLESS derived physical function scores were also compared to scores generated by the full version of WHODAS/HAQ-DI. Results: Of 407 patients,median age was 62 (range 20-93) years, 51% female, 75% Caucasian, with a median EQ-5D-3L index score of 0.83 (0.31-1.00), and WHODAS of 6.0 (0-37). Of cancer sites, 14% had breast, 11% GI, 11% GU, 18% head/neck, 10% thoracic, 18% hematologic, and 15% gynecologic cancers. 34% were stage III-IV; 72% were treated for curative intent. Using the sum of mobility, self-care, and usual activities dimensions of the EQ-5D-3L to dichotomize patients as with or without difficulty, we found an area under the receiver operating curves that was > 90% when comparing to recommended WHODAS and HAQ cut-offs for significant physical dysfunction. Using BLESS, 38% of our clinic outpatients need answer only 5 questions, generating derived-WHODAS scores with no median difference when compared to reported scores; sensitivity of screeners for each WHODAS/HAQ-DI item ranged from 92-100%. Overall, median number of questions asked was 9. Conclusions: By focusing on relevance, BLESS maintained high sensitivity with a dramatic decrease in question burden compared to traditional research surveys for physical function. BLESS is suitable for use in immediate, routine screening of chronic care outcomes.
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.004 | 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".