Reasons for not completing postvasectomy semen analysis.
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
OBJECTIVE: To identify noncompliance rates for 3-month postvasectomy semen analysis (PVSA) in men who have undergone vasectomy and to explore the self-reported reasons for not completing the 3-month PVSA. DESIGN: Retrospective chart review followed by semistructured telephone interviews. SETTING: Two family medicine clinics in Saskatoon, Sask. PARTICIPANTS: Men from the clinics who had undergone vasectomy since 2009. A total of 99 patients completed telephone interviews. METHODS: After a review of electronic medical records at 2 family medicine clinics, patients who had undergone vasectomy since 2009 were identified. Upon review of their charts, the number of patients who did not have PVSA results on file was determined. Some of these men were contacted with a predetermined telephone script to discuss reasons for noncompliance. MAIN FINDINGS: The combined noncompliance rate for the 2 clinics was high (60.5%). Three main reasons for not completing the PVSA were identified among the patient responses. These included patients feeling too busy to complete PVSA, patients feeling confident in the physician or procedure immediately after vasectomy, and patients feeling the PVSA process was too inconvenient. Our high noncompliance rates are consistent with other literature. However, the findings might also have been affected by the proportion of patients who had completed their PVSA who were not included in the telephone sample. Rates differed between the 2 clinics; the clinic with the higher compliance rate acts as an academic practice, with more time for appointments and fewer patients being referred from other physicians. CONCLUSION: Noncompliance rates for PVSA in this study were high. Three main reasons for noncompliance were identified that might help guide counseling opportunities in the future.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.000 | 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 it