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Histology Retention in a Medical School Curriculum

2020· article· en· W3017137483 on OpenAlexaff
Catherine Will, Anna Edmondson, Alexa Hryniuk

Bibliographic record

VenueThe FASEB Journal · 2020
Typearticle
Languageen
FieldEngineering
TopicAnatomy and Medical Technology
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsHistologyVirtual microscopyMedicineCurriculumMedical educationPsychologyPathologyPedagogy

Abstract

fetched live from OpenAlex

Introduction Poor retention of medical knowledge is a concern within medical education. While studies show that student retention from basic science disciplines often follows the “forgetting curve”, histology retention has not been examined independently of other anatomical sciences. Investigation of histology retention is of increasing importance as medical education moves towards integrated curricula with the use of technological advances in the classroom, such as virtual microscopy. The purpose of this study was to evaluate histology retention of first‐year students at the Medical College of Georgia (MCG). Aims The specific aims of this study include evaluating the relationships of 1) histology retention and academic performance 2) retention intervals (RI) to histology retention and 3) histology retention and students’ previous exposure to histology as well as their modality of study. Methods Academic performance data from histology quizzes and exams were collected from first‐year medical students at MCG from the Class of 2022 (n=171). A histology comprehensive assessment was administered at the end of the academic year to assess histology knowledge retained throughout first‐year histology curriculum. Students were also surveyed on their prior histology experience and study method modality. A linear regression analysis was performed to determine if there was a correlation between academic performance and retention. A comparison of means was used to assess the relationship between histology retention scores compared to academic performance in terms of RI, histology exposure, and modality of study. Paired sample t‐tests were used for analyses. IRB approval (exempt) was obtained from Augusta University. Results First‐year medical students at MCG were found to only retain 52.4% ± 17.0% of histology content on the end of year comprehensive assessment. Academic performance in histology did not predict retention at the end of the academic year (R=0.27). Student retention dropped on average from 84% to 52% regardless of RI length (2, 3, 5, or 6 months). No significant difference was found between students with prior histology exposure (85.9% ± 4.9%) and those without (84.2% ± 5.3%) on overall histology grade averages. However, those with prior histology experience did score significantly better on the comprehensive assessment (58.5% ± 15.3% vs. 51.2% ± 17.2%; p=0.04). No significant difference was seen on average histology grades (84.5% ± 4.2%, 84.2% ± 16.6%) or comprehensive assessment performance (52.9% ± 6.9%, 51.4% ± 17.8%) when study modalities (virtual microscopy vs physical slides) were compared. Discussion and Conclusions This data supports previously reported findings that medical students retain on average ~50% of their basic science knowledge. These findings also demonstrate that academic performance is not a predictor of retention. Furthermore, RI and method of study appear to have no significant impact on histology retention. However, prior histology experience appears to aid in retention and suggest that more testing/re‐exposure during the academic year could increase histology retention. These results may be useful in informing educators in medical education and help to guide reform for better retention outcomes from pre‐clinical medical curricula.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.615
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.011
GPT teacher head0.224
Teacher spread0.213 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreEmpirical

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".

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Citations4
Published2020
Admission routes1
Has abstractyes

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