Lymphadenopathy: Defining a palpable lymph node
Bibliographic record
Abstract
BACKGROUND: The threshold size required to detect lymphadenopathy via palpation has never been formally determined. The purpose of this study was to determine the threshold, sensitivity, and error of node palpation and how this changes with experience. METHODS: Lymphadenopathy models were created using polyvinyl alcohol cryogel (PVA-C) to mimic tissue tactility. Node diameter ranged from 0.5 to 4 cm. Study subjects were medical students, otolaryngology residents, and otolaryngology consultants. Each subject provided 22 estimates of size. Primary outcomes were the sensitivity, error (true vs estimated size), and threshold of palpation. RESULTS: Thirty subjects completed the study. Sensitivity was 60%, 74%, and 86% for students, residents, and consultants, respectively (p < .01). Error was 0.88 cm, 0.61 cm, and 0.57 cm, respectively (p < .05). Palpation threshold was 1.32 cm, 0.83 cm, and 0.75 cm, respectively (p < .05). All participants detected nodes ≥2 cm, whereas consultants detected nodes ≥1 cm. CONCLUSION: Experience is associated with decreased palpation threshold and error, and increased sensitivity. Educational interventions should target nodes <2 cm.
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 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".