Colposcopists' practice patterns in Latin America: An international cross‐sectional survey
Bibliographic record
Abstract
Cervical cancer is a public health issue worldwide. Colposcopy is a key tool in the early diagnosis of preinvasive disease. Its practice is heterogeneous due to variations in its performance and the training of professionals. This study aimed to describe colposcopy practice patterns among Latin American gynecologists. A web survey was conducted among colposcopists. A 60-item questionnaire was designed and piloted with 40 physicians. The survey was conducted online voluntarily through a link that was sent to 2217 gynecologist members of the Latin American Federation of Lower Genital Tract Pathology and Colposcopy (FLPTGIC) between April 2024 and January 2025. The survey was answered by 787 colposcopists, with a response rate of 35.49%. The majority were from Mexico (17.92%); 60.61% were women between 30 and 50 years of age. Almost all practitioners (98.68%) performed cervical assessments after applying acetic acid, and 49.94% used Lugol's iodine routinely. More than 90% examined the vulva, and 68.74% examined the vagina during colposcopy. Fewer than half of the participants always reported the colposcopic diagnosis according to the Rio 2011 Colposcopy Nomenclature, and three-quarters reported whether the squamocolumnar junction was visible. More than one-third (37.48%) followed the American Society of Colposcopy and Cervical Pathology guidelines, and 3.2% used International Federation for Cancer Prevention and Colposcopy terminology. When diagnosing dysplasia, more than 90% performed resective methods (91.74%), and colposcopy is the main follow-up strategy. There is a high degree of heterogeneity in training, colposcopic practice patterns, and therapeutic decisions in cases of preinvasive disease among Latin American professionals, even though several scientific associations have established standards and guidelines.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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, 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".