The diagnosis “failure to thrive” and its impact on the care of hospitalized older adults: a matched case-control study
Bibliographic record
Abstract
Background: “Failure to thrive” and associated diagnoses are non-specific terms applied to older adults when there is lack of diagnostic clarity and imply an absence of medical acuity. We investigated the effect of such admission diagnoses on delivery of patient care in a cohort of older adults admitted to a tertiary care teaching hospital. Methods: Retrospective matched cohort study conducted at a tertiary care hospital in Vancouver, BC. Cases identified were adults aged ≥65 years admitted to acute medical wards with an admission diagnosis of “failure to thrive”, “FTT”, “failure to cope”, or “FTC”, between January 1, 2016 and November 1, 2017 (n = 60, median age 80 years). Age-matched controls met the same inclusion criteria with admission diagnoses other than those of interest (n = 60, median age 79 years). Results: The primary outcome was time to admission, measured from time points in the emergency room that spanned from triage to completion of admission orders. Secondary outcomes were concordance of admission and discharge diagnoses and length of stay in hospital. The total time from triage to admission for older adults admitted with FTT and associated diagnoses was 10 h 40 min, compared to 6 h 58 min for controls (p = .02). Concordance of admission and discharge diagnoses was only 12% for the “failure to thrive” cohort, and 95% for controls. Notably, 88% of the “failure to thrive” cohort had an acute medical diagnosis at the time of discharge. Patients in this cohort stayed 18.3 days in hospital compared to 10.2 days (p = .001). Conclusions: Patients with an admission diagnosis of FTT or other associated diagnoses had significant delays in care when presenting to the emergency room, despite often having acute medical conditions on presentation. The use of this non-specific label can lead to premature diagnostic closure and should be avoided in clinical practice.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".