The Lake Louise Score: A Critical Assessment of Its Specificity
Bibliographic record
Abstract
Moore, James, Martin J. MacInnis, Jon Dallimore, and Matt Wilkes. The Lake Louise Score: A Critical Assessment of Its Specificity. High Alt Med Biol . 21:237–242, 2020. Introduction: The Lake Louise Score (LLS) has low specificity for diagnosing acute mountain sickness (AMS). As this tool is used for research and clinical decision making, it is important to understand the origins of this poor specificity. We reviewed AMS diagnoses in a population trekking at low altitude (“false positives”) to critically assess LLS specificity. Method: We retrospectively analyzed data from a sample of 123 adolescents trekking at low altitude to establish the predominant causes of false-positive AMS diagnoses (1993 LLS criteria), separately removing each LLS component to assess its contribution to the final score. Exploratory factor analysis (EFA) was applied to the data to establish component patterns. Results: Removal of LLS components individually showed fatigue contributed slightly more to false-positive AMS diagnoses than sleep quality in this group. An EFA from morning data highlighted sleep quality as a stand-alone factor in the measurement of AMS. Although of smaller significance, an EFA of the evening data highlighted fatigue and headache as the stand-alone factor. Conclusion: Our findings not only supported the recent removal of sleep quality from the LLS, but also demonstrated that fatigue had an equal part to play in the misdiagnosis of AMS in this population. These data highlighted the poor specificity of the LLS and suggest that the measurement of illness at altitude undergo further review.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".