Telehealth for outpatient spine consultation: What do the patients think?
Bibliographic record
Abstract
Objectives We aimed to identify patient specific characteristics associated with a favourable telehealth experience in patients undergoing outpatient spine consultation. Methods We enrolled consecutive patients undergoing telehealth spine consultation during the initial months of the COVID 19 pandemic . We used an online, patient reported survey that collected demographic and disease specific information, as well as validated patient reported outcome measures. Survey items also assessed patients’ perspective of their telehealth experience. We performed univariate analysis to assess for any relationship between patient satisfaction and demographic and disease specific factors, and also collected qualitative responses regarding telehealth. Results 170 unique responses were collected. 35.8% of patients were satisfied with telehealth. When stratified into satisfied (n = 61) and unsatisfied (n = 109), female patients were exclusively unsatisfied with their experience (100% unsatisfied vs male patients 30% unsatisfied, p < 0.01). The groups were similar in terms of age, travel burden, and disease severity. Qualitative responses focussed on the patients’ concerns being able to adequately express their symptoms and functional limitations. Conclusions Patients attending outpatient spinal consultation via telehealth reported a satisfaction rate of 35.8%. Female patients were less likely to be satisfied with telehealth. Patients described concerns being able to express themselves via telehealth. This may be amenable to further study and intervention as telehealth becomes a more prominent part of spinal care.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.004 | 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".