PATI: Patient accessed tailored information: A pilot study to evaluate the effect on preoperative breast cancer patients of information delivered via a mobile application
Bibliographic record
Abstract
OBJECTIVES: The information needs of cancer patients are highly variable. Literature suggests an improved ability to modulate personalised stress, increased patient involvement with decision making, greater satisfaction with treatment choices and reduced anxiety levels in cancer patients who have access to information. The aim of this project was to evaluate the effects of a mobile information application on anxiety levels of patients undergoing surgery for breast cancer. MATERIALS AND METHODS: An application was developed for use with Apple iPad containing information on basic breast cancer biology, different treatments used and surgical techniques. Content and face validity studies were performed. A randomized control trial was designed, with a 1:2 allocation. Data collected include basic demographics and type of surgery. Questionnaires used included: the HADS, Mini-MAC, information technology familiarity and information satisfaction. RESULTS: A total of 39 women participated. 13 women had access to an iPad containing additional information and 26 women acted as controls. The mean age was 54 and technology familiarity was similar among both groups. Anxiety and depression scores at seven days were significantly lower in control patients without access to the additional information provided by the mobile application (p = 0.022 and 0.029 respectively). CONCLUSION: Anxiety and depression in breast cancer patients is both multifactorial and significant, with anxiety levels directly correlating with reduced quality of life. Intuitively, information should improve anxiety levels, however, we have demonstrated that surgical patients with less information reported significantly lower anxiety. We advise the thorough testing and auditing of information initiatives before deployment.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".