Multifactorial Causes of Failure to Thrive in a 79-Year-Old Male
Bibliographic record
Abstract
We discuss a case of a 79-year-old Caucasian male with a multifactoral etiology of failure to thrive. Without having any significant past medical problems, the patient’s failure to thrive condition initiated following an acute stroke. Over the course of this and future hospitalizations, as well as inpatient care, numerous factors contributing to his failure to thrive were identified. These included: malnutrition, post stroke debility, dysphagia, anemia, depression and malignancy. This report serves to document a complicated case of failure to thrive in a geriatric patient with focused discussion of some etiological contributions to a failure to thrive condition. Additionally, we will describe some salient features pertaining to the clinical management of such cases. J Med Cases. 2014;5(10):549-553 doi: http://dx.doi.org/10.14740/jmc1923w
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".