Pathology perspective on gynaecologic malignancy screening questions in electronic consultation
Bibliographic record
Abstract
INTRODUCTION: The electronic consultation service, eConsult, is an asynchronous web-based platform for provider-to-provider consultation with specialists. This study described the utilization of eConsult by primary care providers to obtain specialist opinion in gynaecologic malignancy screening, with a specific focus on pathology-related inquiries. METHODS: This is a cross-sectional retrospective review of eConsults submitted to obstetrics/gynaecology between September 2011 and December 2016. All questions pertaining to gynaecologic cancer screening and their pathologies were included. Each question was classified based on a pre-determined taxonomy. The mandatory primary care providers' exit surveys were analysed to determine eConsult's influence on patient care, primary care providers' referral patterns, primary care providers' satisfaction and educational value. RESULTS: In total, 1,357 electronic consultations were submitted to the obstetrics and gynaecology service during the study period, of which 329 met inclusion criteria. Indications for a screening test based on patient risk factors made up 36% of consults pertaining to gynaecologic malignancy screening and 17% were inquiries about test intervals based on previous results. Primary care providers pointed out gaps in current screening guidelines. In total, 38% of primary care providers reported the eConsult service helped avoid a specialist referral, whereas 47% of primary care providers received new or additional courses of action. Pathology report interpretation accounted for 5% of eConsults and 6% of primary care providers wished for clarification of incidental pathology findings. CONCLUSION: This study uncovered areas of uncertainty among primary care providers regarding gynaecologic cancer screening and gaps in current clinical guidelines. Furthermore, the role of pathology consultants in an eConsult platform is explored and may be extrapolated into practice.
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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 | 0.006 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.009 | 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".