Use of Teleconsultations in a Regional Stereotactic Radiosurgery Service: Pilot Study
Bibliographic record
Abstract
BACKGROUND: The National Health Service Long Term Plan details plans to make digital interactions available to all patients in 5 years. Teleconsultations can improve access to specialist services; however, there is a lack of evidence for the use of teleconsultations in an oncology setting in the United Kingdom. OBJECTIVE: We aim to describe a service evaluation of teleconsultations for patients attending a regional brain metastases clinic. These patients have unique travel restrictions that prevent them from driving. METHODS: From April to October 2018, all patients attending the brain metastases clinic were offered the choice of teleconsultation in place of a face-to-face appointment. Feedback was assessed using a satisfaction questionnaire, and data of all clinic attendances were collected. RESULTS: A total of 69 individual patients had 119 appointments over the duration of the pilot, of which 36 (30.2%) were new patient appointments and 73 (61.3%) were follow-ups. Of the 69 patients, 24 (35%) took part in teleconsultations (41/119, 34.5%). User satisfaction was high, and no patients who took part in a teleconsultation reverted to face-to-face appointments. These patients avoided 2521 miles (61.6 miles per appointment) of hospital-associated travel and travel costs of £441.48 (US $599.83) to £10.78 (US $14.65) per appointment. CONCLUSIONS: Teleconsultations appear to be acceptable in this cohort of patients with brain metastases attending a regional stereotactic radiosurgery service with the potential for significant savings in travel and expenses.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".