QUANTITATIVE PATIENT PREFERENCE EVIDENCE FOR HEALTH TECHNOLOGY ASSESSMENT: A CASE STUDY
Bibliographic record
Abstract
OBJECTIVES: We conducted a systematic review of quantitative research regarding patients' preferences, perspectives and values for ventilation among chronic obstructive pulmonary disease (COPD) patients. Our objective was to explore the feasibility and desirability of incorporating patient preferences within the health technology assessment (HTA) process by working through a case study. METHODS: Medical and economic databases were searched for studies published in English from 1990 through March 4, 2011. Studies were selected based on title and abstract. Due to the heterogeneity of the studies, data were analyzed using a narrative synthesis approach. RESULTS: Among 1833 identified citations, twelve studies met our inclusion criteria. Ten of these studies pertained to COPD patient preferences for ventilation. Results indicate that a significant proportion of COPD patients are willing to forgo ventilation, particularly when it is expressed as "indefinite life support" (60-78 percent) rather than as temporary modality. Results indicate that patient preferences for mechanical or noninvasive ventilation cannot be predicted by covariates (e.g., age, quality of life) or by others who are frequently called upon to make decisions are their behalf. CONCLUSIONS: We found that it is indeed feasible to conduct a systematic review of quantitative preference-related evidence for an HTA topic. However, the process of conducting this preference-related case study also revealed several challenges because there is a high degree of variation in taxonomy, instrumentation, and study design. Therefore, we do not recommend it as a routine part of the HTA process, but we suggest that it is a promising area to pursue for preference-sensitive technological decisions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.312 | 0.578 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.009 |
| Bibliometrics | 0.019 | 0.026 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.006 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".