A systematic review of the use of the then-test for evaluating health-related quality of life in cancer patients
Bibliographic record
Abstract
BACKGROUND: The then-test, also known as the retrospective pre- and post-test design method, is a measurement used to evaluate response shift. The method requires patients to assess their previous health-related quality of life (HRQoL) and provide a retrospective judgement based on their current perspectives. The then-test, however, has been criticized for its reliability and validity. The objective of this systematic review is to summarize the current literature for the use of the then-test for evaluating HRQoL in cancer patients and account for potential response shift effects. METHODS: A literature search was conducted in May 2022 using MEDLINE, PubMed, and PsychINFO dated from 2005 to the time of the search. Studies were included if they (I) used the then-test and (II) involved a population of cancer patients (any cancer site). RESULTS: The literature search resulted in 16 studies, published between 2005 and 2020. All studies used the then-test to detect response shifts. The EORTC QLQ-C30 and EORTC QLQ-BR23 questionnaires were the most common assessments used to evaluate HRQoL. Of the 16 articles, 5 exclusively reported on breast cancer, 5 reported on prostate cancer, and the remaining included all cancer sites. Most studies looked at patients undergoing a combination of chemotherapy, radiotherapy, and hormonal therapy. The mean differences between the retrospective assessment at both 3 and 6 months were significant for various quality of life (QoL) dimensions. Patients in some studies recalled their pretreatment HRQoL (then-score) as better than the pretreatment baseline scores and others reported them as worse, both confirming the existence of a response shift. CONCLUSIONS: Our review demonstrates that the then-test measurement tool has been commonly used for the detection of response shift. Newer measures for response shift have become more accepted; the then-test, if used, should be used with caution considering its limitations and the emergence of more advanced methods.
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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 | 0.018 | 0.084 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.009 |
| Bibliometrics | 0.017 | 0.018 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| 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".