Routine physical function assessment through a Branching Logic Electronic Symptom Survey (BLESS) vs. the 32-combined item HAQ-DI + WHODAS (HW) survey: A quality improvement controlled trial.
Bibliographic record
Abstract
136 Background: Routine outpatient physical function assessment can improve quality of care by prioritizing supports for specific patients. However, conventional research surveys including the 32-combined item HAQ-DI + WHODAS (HW) may be burdensome to patients. To streamline this process, we have developed BLESS, an electronic patient reported outcomes software program that utilizes PRO-ECOG and EQ-5D-3L items to screen for physical dysfunction symptoms, with follow-up questions from HW; BLESS has been demonstrated to have high sensitivity and specificity for the complete HW survey. We compared completion times and patient acceptability of BLESS vs. HW. Methods: Adult oncology outpatients of all stages at Princess Margaret Cancer Centre were allocated prospectively and alternately to receive either BLESS or HW on touchscreen tablets. Completion times were recorded and a patient acceptability survey was administered. Results: Of 225 patients analyzed from breast (19%), GI (22%), GU (12%), gynecology (16%), head/neck (12%), skin (6%) and thoracic (13%) cancer clinics, 118 (52%) patients completed BLESS and 107 (47%) completed HW. Median age was 61 (range: 18-97) years; 60% were female, 68% were Caucasian, 44% were stage IV, and 57% were treated with a curative intent. There were no statistically significant differences in demographic and clinical characteristics between the arms analyzed. Median EQ-5D-3L utility was 0.83 (0.28-1.00), median HAQ-DI was 0.13 (0-3.00) and median WHODAS was 8.3% (0-83.3%). Compared to HW, BLESS resulted in a median completion time reduction of 3.9 minutes (32% decrease; p < 0.001). Although most patients in both arms did not find the survey time-consuming nor caused the clinic visit to be more difficult, a greater proportion of BLESS patients held a stronger conviction in their answers (p = 0.02 for both comparisons). Conclusions: In comparison to traditional research tools of HW for assessing physical function,BLESS was associated with shorter administration times and improved patient acceptability, rendering BLESS more suitable for routine clinical use.
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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 | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".