A Rewarding Medical Internship – Documenting the Case of a Colorectal Cancer Patient with Hematochezia and Melena
Bibliographic record
Abstract
During the summer of 2011, I was a medical intern at 463rd People’s Liberal Army (PLA) Hospital in China. The experience did not only challenge me academically, but it also helped me mature as a person. My beginning of this learning journey was filled with setbacks and criticism that disheartened me tremendously, to the point of fighting back tears on the bus ride home. But, as I learned to find motivation from criticism, prepare research information thoroughly to answer the doctors’ questions, and brighten the patients’ day with a genuine smile and a compassionate attitude, I began to earn the encouragement I previously had not deserved. I was also able to follow the case of an anemic colorectal patient who had both hematochezia and melena. My role was to confirm melena without relying on a fecal blood occult test that was compromised due to a procedural mistake. The task required me to integrate my research information to realize how medication could be confounded with disease symptom. Specifically, I was required to discern the black tarry stool of melena from the dark greenish black stool caused by iron supplement treatment. Looking back two years, those seemingly impossible obstacles were just small hurdles compared with what I need to prepare myself for in the future. Obstacles test my ability to understand, apply, analyse, evaluate, and most importantly—create. Obstacles make my life eventful, for I certainly do not intend for it to be as smooth as a straight line on an electrocardiogram.
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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.002 | 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".