Does patient preference for online or telephone follow-up impact on response rates and data completeness following injury?
Bibliographic record
Abstract
BACKGROUND: Routine collection of patient-reported outcomes is needed to better understand recovery, benchmark between trauma centers and systems, and monitor outcomes over time. A key component of follow-up methodology is the mode of administration of outcome measures with multiple options available. We aimed to quantify patient preference and compare the response rates and data completeness for telephone and online completion in trauma patients. METHODS: A registry-based cohort study of adult (16 years and older) patients registered to the Victorian State Trauma Registry and Victorian Orthopedic Trauma Outcomes Registry from April 2020 to December 2020 was undertaken. Survivors to discharge were contacted by telephone and offered the option of telephone or online completion of 6-month follow-up using the five-level EuroQol five-dimension (EQ-5D-5L) questionnaire and the 12-item World Health Organization Disability Assessment Schedule (WHODAS). The online and telephone groups were compared for differences in characteristics, follow-up rates, and data completeness. Multivariable logistic regression was used to identify predictors of choosing online completion. RESULTS: Of the 3,886 patients, 51% (n = 1,994) chose online follow-up, and the follow-up rates were lower for online (77%), compared with telephone (89%), follow-up. Younger age, higher socioeconomic status, and preferred language other than English were associated with higher adjusted odds of choosing online completion. Admission to intensive care was associated with lower adjusted odds of choosing online completion. Completion rate for the EQ-5D-5L utility score was 97% for both groups. A valid total 12-WHODAS score could be calculated for 63% of online respondents compared with 86% for the telephone group. CONCLUSION: More than half of trauma patients opted for online completion. Completion rates did differ depending on the questionnaire and telephone follow-up rates were higher. Nevertheless, given the wide diversity of the trauma population, the high rate of online uptake, and potential resource constraints, the study findings largely support the use of dual methods for follow-up. LEVEL OF EVIDENCE: Prognostic/Epidemiological, Level III.
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 | 0.079 | 0.254 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".