Identifying the Cause of Young Women Diagnosed with Locally Advanced Cervical Cancer Despite Routine Screening
Bibliographic record
Abstract
Background Screening programs for cervical cancer in developed countries have led to a marked reduction in mortality given their ability to detect early stage disease. Despite this success, there remains a concerning number of women compliant with regular screening that are diagnosed with advanced disease – a particularly devastating diagnosis for young women given the sexual and reproductive consequences. To explore the reasons behind this shortcoming, the present study sought to identify diagnostic commonalities amongst young women compliant with screening who presented to a tertiary cancer hospital with locally advanced cervical cancer (LACC). Methods A review of all women (age<50) with LACC receiving definitive chemoradiation between Sept/10‐Dec/12 at our institution was performed to identify those with a routine Pap test done ≤2y prior to diagnosis. Eligible women were offered semi‐structured, face‐to‐face interviews focusing on 4 areas: presenting symptoms, experience with the health care system, feelings after diagnosis, and perception of their future. Interviews were audiotaped, transcribed and a constant comparison analysis was performed to identify key themes. Thirteen out of 38 women (34%) with LACC were compliant with screening prior to diagnosis and met the other study criteria (median age: 38 (27–49)). All had a normal Pap, except one, completed 11 months prior to diagnosis. Ten consented to participate in an interview. Results Several key themes were identified: a lack of understanding about the symptoms/diagnosis of cervical cancer and a belief that LACC does not occur in those compliant with screening, reluctance from health care providers to perform pelvic examinations, the emotional burden of diagnosis on both the patient and their families and lessons learned with a different outlook for the future. Conclusions There is a need to educate physicians and the public about cervical cancer, even where screening is available. Delay in diagnosis has detrimental effects on quality of life and likely prognosis. This abstract is from the Experimental Biology 2018 Meeting. There is no full text article associated with this abstract published in The FASEB Journal .
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".