MétaCan
Menu
Retour à la cohorte
Enregistrement W2056277216 · doi:10.1111/j.1742-1241.2010.02409.x

Evidence-based flying: a new paradigm for frequent flyers

2010· editorial· en· W2056277216 sur OpenAlexaboutno aff
Leslie Citrome

Notice bibliographique

RevueInternational Journal of Clinical Practice · 2010
Typeeditorial
Langueen
DomaineEconomics, Econometrics and Finance
ThématiqueHealthcare Policy and Management
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésNewspaperMedicineCompetition (biology)Number needed to treatEvidence-based medicineAdvertisingAlternative medicineBusiness

Résumé

récupéré en direct d'OpenAlex

In recent years, increasing emphasis has been placed on the practice of evidence-based medicine (EBM). Originally proposed by Sackett et al., EBM exhorts clinicians to incorporate into medical decision-making the best available research evidence together with an individualised assessment and simultaneously considering the preferences of the patient (1). One of the important tools of EBM is the notion of number needed to treat (NNT) (2). The concept of EBM and NNT can be easily translated to help with other human activities, such as airplane travel, especially for frequent flyers. Academics ‘on the circuit’ have their own extensive travel experience (albeit anecdotal), and fairly well defined values and preferences when it comes to airline selection. What has been missing in the flyer decision-making process is robust research evidence. This obstacle to the proper practice of evidence-based flying (EBF) is now disappearing, thanks to cut-throat competition amongst the airlines and advertisements that tout low rates of departure delays. Introducing number needed to fly (NNF): With the disclosure of on-time departure rates, this dichotomous outcome can be used to calculate the number of flights one has to take with one airline vs. another before expecting to encounter (or avoid) one additional departure delay. The data from Table 1 were extracted from a newspaper advertisement in USA Today (ostensibly the most commonly read national newspaper amongst US frequent flyers) (3). NNF was determined by taking the difference in on-time departure rates between the two airlines of interest, calculating the reciprocal, and then rounding up to the next highest whole number. Caveats to this crude measure is that the specific airport one is flying out of is not considered – adjustment for this baseline risk (and others) requires methodological refinements that have yet to be worked out with the data currently publically available. Regardless of the emphatic claims by each airline, the NNF may not be entirely compelling; for example, the comparison between US Airways vs. Delta reveals that about 30 flights would need to be taken to encounter one additional delayed departure. One can easily calculate other pair-wise comparisons for NNF. If the denominators are known, a 95% confidence interval can also be calculated. Although the NNF for the comparison between US Airways (ranked #1 on this list) vs. American (ranked last) is 10, this may still not be a compelling effect size given the perks an American Airlines frequent flyer may enjoy. This latter point can be quantified if one examines another metric outlined below. Introducing number needed to upgrade (NNU): This statistic can only be guesstimated as the actual rates of achieving a successful upgrade from coach to first class can be highly variable, depending on baseline factors such as city from which one is flying, time of day, day of week, class of ticket purchased and individual traveller characteristics such as frequent flyer loyalty club status level. In this author’s experience, flying his airline of choice the NNU is in the range of 2–4. The likelihood to be upgraded or delayed (LUD) can thus be calculated, with the result being of some utility in flying decision-making. In any of these calculations, using any frequent flyer outcome measures, the personal preferences and values of the flyer are key to making flyer-relevant decisions (Figure 1). These include the type of food that is served (free or not), pillow and blanket policy, and so on. Cost considerations (same as alternative airlines or not, extras for checked baggage) and availability of flights also enter the decision-making process. Using baseline characteristics to refine our calculations will help make the NNF and NNU estimates more precise. We all look forward to greater transparency and the posting of delay and upgrade rates in publically accessible airline registries, further enhancing the amount of data available to help us make wise decisions. What is evidence-based flying (EBF)? Leslie Citrome belongs to the loyalty programmes for all of the airlines he flies on and has platinum status with Continental Airlines and silver status with Air Canada. The initial draft of this manuscript was written in the air on a Qantas flight between Los Angeles, USA, and Brisbane, Australia.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,011
score de la tête « metaresearch » (Gemma)0,189
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesMétarecherche, Méta-épidémiologie (sens strict), Intégrité de la recherche
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: Sans objet
GenreSignal candidat: Éditorial · Signal consensuel: Éditorial
Score de désaccord entre enseignants0,178
Score d'incertitude au seuil1,000

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0110,189
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0010,001
Bibliométrie0,0000,000
Études des sciences et des technologies0,0000,000
Communication savante0,0000,001
Science ouverte0,0010,000
Intégrité de la recherche0,0010,003
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,331
Tête enseignante GPT0,504
Écart entre enseignants0,173 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Devis d'étudeSans objet
Domainenon disponible
GenreÉditorial

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations2
Publié2010
Routes d'admission1
Résumé présentoui

Explorer davantage

Même revueInternational Journal of Clinical PracticeMême sujetHealthcare Policy and ManagementTravaux en français237 207