Can the Toronto Extremity Salvage Score Produce Reliable Results When Used Online?
Bibliographic record
Abstract
BACKGROUND: Web-based questionnaires have become popular, however, access to the Internet can be biased regarding age, gender, and education, among other factors. Therefore, it is unknown whether this is a reasonable avenue to administer a questionnaire to patients or whether Web-based can be a reliable alternative to paper-based. QUESTIONS/PURPOSES: We determined whether the Internet version of the Toronto Extremity Salvage Score is reproducible compared with the paper-based version and the compliance and completion rates. PATIENTS AND METHODS: The study population consisted of 81 adults who had had surgery for a musculoskeletal tumor of the lower extremity more than 12 months earlier. The Toronto Extremity Salvage Score was administered by paper at a baseline interview and then readministered via Internet 7 to 14 days later to those with access. RESULTS: Sixty of the 81 patients (74%) were able to use the Internet. Increasing age and lower education levels were correlated with a lower likelihood of using the Internet. Questionnaires were done online and on paper by 56 patients but 10 were excluded because of self-reported change in circumstances. The mean TESS was 85.7 (range, 41.1-100; SD, 17.26) for the paper-based questionnaire and 85.2 (range, 42.5-100; SD, 17.47) for the Internet-based questionnaire. The intraclass correlation coefficient was 0.97. CONCLUSIONS: The questionnaire can be transferred successfully to the Internet and can be used reliably instead of a paper-based instrument. Recruitment to use an Internet-based questionnaire is limited only by the percentage of patients able to access and use the Internet.
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 | 0.007 | 0.042 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".