Performance of the patient reported CAPLET2.0 physical functioning assessment tool.
Bibliographic record
Abstract
194 Background: CAPLET is a published patient reported outcome measure which assesses domains of physical function efficiently in cancer patients through a branching logic algorithm using a patient-reported outcome version of ECOG performance status and EQ5D-3L health utility score (PMID: 29982902; a total of 6 screening questions). We assessed the sensitivity and specificity of this tool after updating it with screening questions from the EQ5D-5L (CAPLET2.0) as opposed to the previously published EQ5D-3L screener (CAPLET). Methods: Eligible cancer patients across all outpatient clinics and disease sites (solid and liquid cancers) at the Princess Margaret Cancer Center completed a questionnaire on touch-screen technology containing the EQ5D-5L, patient-reported outcome (PRO)-ECOG performance scale, the gold standard WHODAS 2.0 (12 items) and HAQ-DI (20 items) physical functioning questionnaires, and a clinico-demographic survey. Results: Of 261 patients, 53% were female, 61% were Caucasian, and 71% had English as a first language. Disease sites included: 12% breast, 10% gastrointestinal, 12% genitourinary, 19% gynecological, 13% head and neck, 13% lung and 13% hematological cancers. The optimal branching logic cut-points were identified when PRO-ECOG, scored as 0-1 and individual EQ5D items scored with the best functioning category allowed specific WHODAS/HAQ-DI questions to be skipped. Against individual WHODAS-HAQ-DI items, CAPLET2.0 had sensitivities ranging from 83-100% (median 93%), and specificities of 50-82% (median 58%). Using CAPLET 2.0, 45% of patients could have skipped all but five questions measuring mental health and cognition which are always asked. Sensitivities, specificities and the proportion of questions that could have been skipped were all similar to the original CAPLET tool. Conclusions: CAPLET2.0, which uses the updated EQ5D-5L and PRO-ECOG as screening questions to assess physical function in cancer patients has comparable performance to the original CAPLET tool. CAPLET2.0 is therefore a viable alternative physical functioning screening tool for both routine and research 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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 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".