Bibliographic record
Abstract
Ms. Patmore is a 51-year-old premenopausal female who presents to her family physician’s office with an ongoing episode of severe depression. She is accompanied by her daughter, who reports her mother’s inability to sleep, a lack of appetite, low energy and speech latency.1 Past medical history is significant for major depressive disorder (MDD) diagnosed five years ago and managed effectively in the past three years with the selective serotonin reuptake inhibitor (SSRI) fluoxetine (20 mg/d). Ms. Patmore’s daughter reports their family’s recent concerns that Ms. Patmore is becoming increasingly depressed, with similar characteristics as her initial depressive episodes five years previously. Ms. Patmore has a recent (1 year) history of estrogen-receptor positive invasive ductal carcinoma (IDC; T1N1 disease) that was identified on routine mammogram. She underwent a unilateral mastectomy 10 months ago and surgical reconstruction is pending. Post-operatively, Ms. Patmore began adjuvant therapy with oral tamoxifen (20 mg/d) as her cancer was found to be estrogen receptor (ER) positive.2 Her daughter reports that at that time, fluoxetine was replaced with the SSRI escitalopram (20 mg/d), owing to the propensity for drug interactions between certain SSRIs (including fluoxetine) and the cytochrome P450 (CYP) 2D6 enzyme, which metabolizes tamoxifen to its active metabolite, endoxifen.3 Over the past three months, Ms. Patmore’s depressive symptoms have returned and worsened.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.345 | 0.183 |
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".