Comparing the use of Arabic decision aid to usual care
Bibliographic record
Abstract
OBJECTIVES: To evaluate the effect of decision aids (DAs) for metastatic colorectal cancer (mCRC) patients in the Arabic language. METHODS: A multi-centered randomized control trial was used to evaluate the effect of Arabic DA use with usual care for mCRC patients compared to usual care alone. Patients were recruited from 4 main oncology centers in Saudi Arabia: King Fahad Medical City, Riyadh; King Khalid University Hospital, Riyadh; King Saud Medical City, Riyadh; and King Fahd Specialist Hospital, Dammam, Saudi Arabia, between March 2016 and October 2018. The final follow up was in April 2019. The study measured patient understanding of prognosis, treatment options, and the level of the patient's anxiety. RESULTS: Ninety-two patients were included in the analysis; 51 in the intervention group. A small proportion of both (DA with usual care and usual care) understood that mCRC was incurable (8% and 5%) of the 2 groups, respectively. There was no significant difference between groups in anxiety level; however, a time effect both initially and after one month was significantly higher than at 6 month. CONCLUSION: The study shows that a higher level of patient's baseline understanding lowered anxiety levels over time. Decision aids group presented low levels of anxiety over time than those provided the usual care. We recommend using Arabic DA in the oncology centers dealing with mCRC patients, aiming to empower patients in decision making.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".