Bibliographic record
Abstract
That Monday morning at the clinic started out like any other—the buzz of nurses directing patients to examination rooms, overhead pages filling the air, and residents milling about before the start of their clinics. A few hours into my morning rounds, I had developed a good rhythm—reviewing the patient’s chart, then recording a history and performing a physical, followed by a review with my preceptor, back to see the patient again, dictating the follow-up letter, and arranging for a follow-up visit. Her name was towards the end of the list that day. “Routine follow-up” was listed as the reason for her visit. My preceptor and I quickly perused her chart before going in—an 82-year-old female with locally advanced colon cancer that had been resected about two years earlier. She had survived her surgery, and no adjuvant therapy was administered. Her chart also made note of “mild to moderate Alzheimer’s.” The radiologist’s notes on her latest CT scan were not reassuring—“lesions most consistent with local recurrence and metastatic disease.” Her blood markers (CEA) were trending upwards and were ominously flagged for being elevated. This was not shaping up to be a routine follow-up visit after all. She was waiting accompanied by her husband when we entered the room. “I’m doing great. I can walk lots. I feel healthy. I have a good appetite,” she replied in response to our first question. We then went on to share the results of her most recent scan and blood tests. Her husband, being hard of hearing, leaned in, his mind and ears focused on what we were telling him. “So what does that mean?” he asked moments after we had told them that the cancer was back, a sign that his cognitive state was not too far behind his wife’s. Our patient had a puzzled and worried look on her face, her eyes darting between us and her husband. She knew something was wrong but couldn’t quite place her finger on it. Wanting to reassure us, she again repeated, “But I feel so good. I can walk. I have a great appetite.” We agreed these were indeed good signs, but inside we knew that her current health would not last for too long. Together, we went over the options for active therapy, and one by one each was ruled out as a possibility. We introduced the couple to the idea of palliative care and psychosocial support and provided the appropriate resources along with a follow-up appointment in the near future. After our encounter, I had a chance to reflect on what had just transpired. Between their medical illnesses and cognitive decline, this couple’s ability to cope with life was teetering on the edge. They were living independently at the time, but that would soon have to change. What started out as routine and predictable drastically changed by the end of the visit. This particular follow-up took a little more than half an hour of my time, but it had thrown the rest of their lives into chaos. It served as a poignant reminder of the responsibility that we as physicians have to take every encounter, however routine it might appear, as one that could have far-reaching ramifications for our patients. George Kurien Christopher de Gara, MB, MS Mr. Kurien is a fourth-year student, University of Alberta Faculty of Medicine and Dentistry, Edmonton, Alberta, Canada; ([email protected]). Dr. de Gara is professor of surgery, University of Alberta Faculty of Medicine and Dentistry, Edmonton, Alberta, Canada.
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 | 0.003 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.114 | 0.038 |
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".