Patient Use of Smartphones to Communicate Subjective Data in Clinical Trials
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
PURPOSE: Various methods have been used in clinical trials to collect time-sensitive subjective responses, including study diaries, telephone interviews, and use of text messaging. However, all of these methods are limited by the uncertainty of when the participants enrolled in the study actually record their responses. This technical note reports on the utility of the BlackBerry smartphone to collect such data and why such a system provides advantages over other methods to report subjective ratings in clinical studies. METHODS: The Centre for Contact Lens Research developed an on-line web-enabled system that permits participants to record and immediately transmit subjective rating scores in numerical form directly into a web-enabled database. This, combined with the utility of BlackBerrys, enabled time-specific e-mail requests to be sent to the study participants and then for that data to be simultaneously transmitted to the web-enabled database. This system has been used in several clinical trials conducted at the Centre for Contact Lens Research, in which data were collected at various times and in several specific locations or environments. RESULTS: In the clinical trials conducted using this system, participants provided responses on 97.5% of occasions to the requests for data generated by the automated system. When the request was for data on a set date, this method resulted in responses of 84.1% of the time. CONCLUSIONS: The series of clinical trials reported here show the benefits of the utilization of the BlackBerry to collect time- or environment-sensitive data via a web-enabled system.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it