Bibliographic record
Abstract
Cervical cancer detection and prevention has paved the way for a myriad of cancer screening programs today. At first, observing cervical lesions was for the purposes of identifying sexually transmitted diseases. Later, however, it was thought that lesions and malignancies were along the same continuum; this led to the adoption of aggressive treatments of early, benign lesions. Stagnation of the cure rates in the late 1920s was resolved by the development of the Pap smear, a procedure still in use today. Although widespread adoption of the Pap smear led to identification of cervical lesions, subjectivity and lack of standardization presented further obstacles to achieving the optimal screening program. Meanwhile, organizations invested in women’s health advocated strongly for women to get regular gynecological check-ups. While the medical community was moving forward with identifying lesions, the cause of cervical cancer was still up for debate. From recognizing that cervical cancer was rare in nuns to assuming trauma played a part, the scientific community finally concluded that HPV wasthe causative agent. Today, the goal is to prevent cervical cancer with vaccines rather than treat lesions when they’re found. The journey of cervical cancer has certainly been an arduous one, and there is still much to be improved as treatments and target populations for vaccines are still up for debate.
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 | 0.008 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.013 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.005 | 0.012 |
| Insufficient payload (model declined to judge) | 0.077 | 0.035 |
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".