Monitoring a Remote Phototherapy Unit via Telemedicine
Bibliographic record
Abstract
Background: The delivery of effective phototherapy to patients with psoriasis living in areas devoid of dermatologists is difficult. Telemedicine has proven useful in the delivery of health care in such locations. Objective: This evidence-based study sought to investigate the use of telemedicine in the monitoring of phototherapy of psoriasis patients located in a Nova Scotia region with no dermatologist. Methods: Psoriatic patients were reviewed six months before and after protocols and monitoring were instituted. First, charts of 23 patient treated with phototherapy were reviewed from the Aberdeen Hospital in New Glasgow. Patients were either self-referred or referred by a family physician and occasionally a dermatologist. Treatments were not monitored by a specialist. Second, a group of 33 patients receiving treatment were supervised via telemedicine by a dermatologist 250 km away in Halifax. Results: During the study period, treatment time decreased from 140 to 37 days. In the monitored group, 40% more patients were clear of psoriasis at time of discharge. The number of patients with side effects decreased. The number of self-and family practice–referred patients dropped; the clinic became a referral center for dermatologists. Conclusion: Telemedicine provided an excellent way to monitor patients receiving phototherapy in an area without a dermatologist. Overall, patient care improved: More patients were treated effectively with better outcomes and fewer side effects.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".