Telephone consultations in urology: Who, when, where, and why?
Bibliographic record
Abstract
INTRODUCTION: Telephone consultations are part of a strategy to allow access to medical expertise. Telephone consultations have been fee-for-services benefits in the province of Quebec since 2012. Recent studies have shown that adequate communication is one of the most common means to prevent disability and death. We sought to determine the characteristics of phone consultations made to a tertiary centre's urologists and to characterize their experience. METHODS: We performed a prospective study using all billing receipts filed by 15 academic urologists for phone consultations received during a 10-month period. A descriptive analysis was done to collect the principal characteristics of all phone calls received. Moreover, an online survey was distributed to those urologists. The survey was composed of 10 multiple-choice questions to review their personal experience. RESULTS: A total of 678 billing receipts were analyzed. The most common reasons for calls were lithiasis (11.5%), hematuria (10.5%), and urinary retention (8.4%). Most phone calls (57.7%) were made by emergency physicians and family doctors. The majority (88.7%) of calls were placed between 8:00 am and 5:00 pm. Most of the calls came from the immediate region covered by the group. Our survey demonstrated that urologists pay more attention to document telephone consultations since the introduction of the new remuneration plan. Most urologists found the phone consultations to be relevant. CONCLUSIONS: Lithiasis and hematuria are the primary reasons for telephone consultations. Continuing medical education on these subjects could be worthwhile. The RAMQ remuneration plan has improved documentation of phone consultations by urologists.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".