Case 2: Green vaginal discharge in a 7-year-old girl
Bibliographic record
Abstract
A seven-year-old girl accompanied by her mother presents to your community clinic with a three-day history of green vaginal discharge. She reports no other symptoms. You perform a full history and physical examination, including inspection of the external genitalia. The genital examination is entirely normal. A swab of the discharge is sent for culture and comes back positive for Neisseria gonorrhoeae. You discuss the findings and the possibility of sexual transmission with the patient’s mother, who has no concerns of sexual abuse. You report the findings to public health and the local child welfare agency. Joint investigation by child welfare and the police is inconclusive (the child denies any inappropriate sexual contact). You treat her infection and screen for other sexually transmitted infections (STIs) including chlamydia, hepatitis, syphilis and HIV. At a follow-up visit, the mother asks if she can speak with you in private. She says she doesn’t believe that anything has happened to her daughter because she seems fine. On further questioning, however, she does report that her daughter has recently had a few episodes of crying out in the night. How would you counsel this mother and child?
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 | 0.000 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.008 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".