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Record W2341406742 · doi:10.1055/s-0035-1564596

Impact of Surgical Waitlist on Quality of Life

2015· article· en· W2341406742 on OpenAlexaff
Olufemi R. Ayeni, Forough Farrokhyar, Dyda Dao, Rick Ogilvie, Devin Peterson, Lauren E. Salci

Bibliographic record

VenueThe Journal of Knee Surgery · 2015
Typearticle
Languageen
FieldMedicine
TopicKnee injuries and reconstruction techniques
Canadian institutionsMcMaster University
Fundersnot available
KeywordsMedicineAnterior cruciate ligamentACL injuryOrthopedic surgeryQuality of life (healthcare)Anterior cruciate ligament reconstructionFeelingPhysical therapyAnterior Cruciate Ligament InjuriesModalitiesSurgeryNursing

Abstract

fetched live from OpenAlex

Prolonged surgical wait times have been associated with reduced quality of life (QoL) in patients requiring orthopedic surgery. However, the effects on patients awaiting anterior cruciate ligament (ACL) reconstruction surgery remains to be established. Here, it is determined that being on a waitlist for ACL reconstruction surgery reduces patients' QoL through negatively impacting disability, physical, and emotional health. A survey assessing patients' disability, physical, and emotional health was administered to 50 patients on the waitlist for ACL reconstruction surgery. Data were divided into two groups based on the wait time for surgery: ≤ 182 and > 182 days. Patients on the waitlist > 182 days either lost their job or had it significantly modified more often than those waiting ≤ 182 days. A total of 63% of the respondents reported feeling that their overall physical health deteriorated significantly or somewhat due to their ACL injury. A total of 51% of all patients reported feeling sad/depressed all or most of the time because they were not able to participate in their main sport due to their ACL injury. Our findings provide evidence that the wait times for ACL reconstruction surgery be reduced or nonoperative modalities be prescribed to mitigate the negative impact that prolonged surgical wait times have on patients' QoL.

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)0.004
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.132
Threshold uncertainty score0.173

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.078
GPT teacher head0.371
Teacher spread0.294 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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".

Quick stats

Citations22
Published2015
Admission routes1
Has abstractyes

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