Exploring Differences in Interpretation of Words Essential in Medical\n Expert-Patient Communication
Bibliographic record
Abstract
In the context of cancer treatment and surgery, quality of life assessment is\na crucial part of determining treatment success and viability. In order to\nassess it, patients completed questionnaires which employ words to capture\naspects of patients well-being are the norm. As the results of these\nquestionnaires are often used to assess patient progress and to determine\nfuture treatment options, it is important to establish that the words used are\ninterpreted in the same way by both patients and medical professionals. In this\npaper, we capture and model patients perceptions and associated uncertainty\nabout the words used to describe the level of their physical function used in\nthe highly common (in Sarcoma Services) Toronto Extremity Salvage Score (TESS)\nquestionnaire. The paper provides detail about the interval-valued data capture\nas well as the subsequent modelling of the data using fuzzy sets. Based on an\ninitial sample of participants, we use Jaccard similarity on the resulting\nwords models to show that there may be considerable differences in the\ninterpretation of commonly used questionnaire terms, thus presenting a very\nreal risk of miscommunication between patients and medical professionals as\nwell as within the group of medical professionals.\n
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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 | 0.016 | 0.164 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".