Bibliographic record
Abstract
Twenty-nine years ago I was born with Short Bowel Syndrome, which required me, from birth, to depend on feedings through a gastrostomy tube and central line. To this day I require IV nutrition daily. I am not yet thirty years old, and I have had 65 surgeries, including a small intestine organ transplant, and bilateral hip replacements. I understand how the medical system works—and where it fails. I now have an incomparable level of knowledge and first-hand experience with our healthcare system that I hope to use to enlighten and educate.My care for the last three decades has not been whole-body. Instead, it has been fragmented highly subspecialized care, leaving me doing well in the short term, but with no cohesive long-term plan. I had discussions with my current medical care providers, I left teams that were inadequate, I inquired opinions of new doctors from around the country and curated my own team of professionals from 3 states in order to LIVE not survive in a hospital. The result: Whole person care with me as the driver and coordinator.I will impart pieces of my experience to understand what it takes to create the “pit crew,” the healthcare system was not providing me, and how you as a medical provider can be the best advocate for your patient on their “pit crew.” Hear real examples of patient-provider interactions, the vital importance of shared decision making, and the details behind true compassion that inspire whole person care.
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 | 0.004 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.024 | 0.006 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.008 | 0.031 |
| Insufficient payload (model declined to judge) | 0.015 | 0.005 |
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".