Hysteroscopy needs of indigenous communities in Northern Quebec: a retrospective cohort study
Bibliographic record
Abstract
We aimed to determine the surgical output for patients from Nunavik undergoing transfer to an urban centre for hysteroscopy, and associated costs. We performed a retrospective chart review of all patients from the 14 villages of Nunavik transferred for hysteroscopic surgery from 2016 to 2021. Diagnoses, surgical intervention, and nature of the procedure were all extracted from the patient charts, and costs/length of stay obtained from logisticians and administrators servicing the Nunavik region. Over a 5-year period, 22 patients were transferred from Nunavik for hysteroscopy, of which all were elective save one. The most common diagnosis was endometrial or cervical polyp and the most common procedure was diagnostic hysteroscopy. The average cost for patient transfer and lodging to undergo hysteroscopy in Montreal ranged from $6,000 to $15,000 CDN. On average, 4-5 patient transfers occur annually for hysteroscopy, most commonly for management of endometrial polyps, at a cost of $6,000 to $15,000 CDN, suggesting the need to investigate local capacity building in Nunavik and assess cost-effectiveness.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: yes | Observational | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: yes | Observational | high |
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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, unvalidatedLabeled directly by 2 models reading the full record.
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".